Expert Physicians Research Articles

Cost-effectiveness of implant movement analysis in aseptic loosening after hip replacement: a health-economic model

ObjectiveTo investigate the cost-effectiveness of using Implant Movement Analysis (IMA) to follow up suspected aseptic loosening when the diagnosis after an initial X-ray is not conclusive, compared with a diagnostic pathway with X-ray follow-up.MethodsA health-economic model in the form of a decision tree was developed using quality-adjusted life years (QALY) from the literature, cost-per-patient data from a university hospital and the probabilities of different events from expert physicians’ opinions. The base case incremental cost-effectiveness ratio (ICER) was compared with established willingness-to-pay thresholds and sensitivity analyses were performed to account for assumptions and uncertainty.ResultsThe base case ICER indicated that the IMA pathway was cost effective (SEK 99,681, compared with the SEK 500,000 threshold). In the sensitivity analysis, the IMA pathway remained cost effective during most changes in parameters. ICERs above the threshold value occurred in cases where a larger or smaller proportion of people receive immediate surgery.ConclusionA diagnostic pathway using IMA after an inconclusive X-ray for suspected aseptic loosening was cost effective compared with a pathway with X-ray follow-up.

Wavelet transform and deep learning-based obstructive sleep apnea detection from single-lead ECG signals.

Sleep apnea is a common sleep disorder. Traditional testing and diagnosis heavily rely on the expertise of physicians, as well as analysis and statistical interpretation of extensive sleep testing data, resulting in time-consuming and labor-intensive processes. To address the problems of complex feature extraction, data imbalance, and low model capacity, we proposed an automatic sleep apnea classification model (CA-EfficientNet) based on the wavelet transform, a lightweight neural network, and a coordinated attention mechanism. The signal is converted into a time-frequency image by wavelet transform and put into the proposed model for classification. The effects of input time window, wavelet transform type and data balancing on the classification performance are considered, and a cost-sensitive algorithm is introduced to more accurately distinguish between normal and abnormal breathing events. PhysioNet apnea ECG database was used for training and evaluation. The 3-min Frequency B-Spline wavelets transform of ECG signal was carried out, and Dice Loss was used to train the classification model of sleep breathing. The classification accuracy was 93.44%, sensitivity was 88.9%, specificity was 96.2% and most indexes were better than other related work.

Nixon S. See, MD (1962-2023)

While I am honored to be chosen among his many friends to pay tribute to him, I am struck by ambivalence, as I mourn just as deeply for Nixon’s passing. Nevertheless, as this is likely my last opportunity to praise this great man, I aim to give a good accounting of his life and times.
 Nix and I first met as applicants of the residency program in ORL-HNS at the Ospital ng Maynila Medical Center (OMMC) in September 1989. Subsequently, he established his practice in his hometown of Calapan, Oriental Mindoro. He was briefly affiliated with the PSO-HNS Southern Tagalog Chapter as a charter member but was forced to relinquish this and a flourishing practice and return to Manila to care for his wife. He joined the Fatima University Medical Center and College of Medicine, eventually becoming chairman of the Department of ORL-HNS. He rejoined OMMC as consultant in 2010.
 Having been his close friend for more than three decades, I have known him to be a peaceful man. Indeed, he abhorred life’s unnecessary complexities, wanting only a simple, quiet life, content in the knowledge that he has filled the cups of those whose lives he had touched.
 I use PEACEFUL, not only in a literal sense, but also as an acronym that represents his qualities I have learned to love, as well as hate. Nix was a Principled man, to the point of being stubborn. His moral compass was always straight and true, unwavering, and without any gray areas. He was also a Practical man, choosing to be frugal for himself, yet generous to his family. He was Exacting, meticulously insisting on accomplishing tasks in a specific, almost textbook-like manner. “So-so” was not in his vocabulary. An Amiable individual, he was never aloof and was ready to greet anyone with a genuine smile. He chose to live Cleanly, leading a life not only free of vices, but one pursued with healthy physical activity. Being meticulously principled was not without its drawbacks. He was Cranky. During a typical cantankerous mood, he would wipe his face with his palm, as if to “erase” it. This was followed by a long, deep snort. Woe to the junior resident who would earn his ire! I would remember him say: “though I’m a specialist, I don’t think I will ever become filthy rich.” He was a man of charity, an Expert physician: competent, conscientious, caring, and humane. He was a devout Family man, one whose decisions, without exception, revolved around the welfare of his loved ones. While he reached impressive heights professionally, he remained grounded and Unassuming, a true epitome of humility. Lastly, he was a Loyal friend to the end.
 I have lost a brother. Yet, during his final days, having witnessed how his illness ravaged his physical form relentlessly, mercilessly, I take solace in the knowledge that he is now in a place of eternal serenity, free of doubt, fear, and despair. I once read: God is indeed wise because He placed death at the end of life and not at its beginning. Truer words have never been said, for it allowed me to experience the richness of life with Nix. To my friend, I say: “Till we meet again.”

Heart sound classification based on bispectrum features and Vision Transformer mode

In regions with limited resources and moderate incomes, the relentless spectre of cardiovascular diseases (CVDs) continues to loom large. Amidst this challenge, the precise classification of heart sounds is emerging as a pivotal linchpin in the realm of early CVD diagnosis and intervention. Manual heart sound auscultation efficacy remains tethered to the expertise of physicians, but the tides are shifting. With deep learning algorithms, heart sound classification reaches new heights. In this paradigm-shifting article, we unveil an ingenious model fortified by bispectrum-inspired feature extraction and the cutting-edge prowess of the Vision Transformer (ViT) model. This model spearheads the binary classification of heart sounds, labelling them as either 'normal' or 'abnormal.' Our model uses data from the PhysioNet Challenge 2022 database, which contains 3163 data points from 942 patients. The model showcases an adept classification process with a remarkable consistency, notably holding its own when distinguishing between heart sounds of pregnant and nonpregnant patients. Moreover, we dare to challenge the status quo. This article boldly pits the performance of our model against that of seasoned cardiologists. Our model emerges as the triumphant frontrunner, eclipsing the proficiency of even the most seasoned cardiologists. In a world where health resources are unevenly distributed, this pioneering work offers a beacon of hope, unlocking a future where advanced algorithms not only match but surpass human expertise. Step into the forefront of transformative cardiac care—where artificial intelligence becomes the key to unravelling the secrets of heart sounds.

Leveraging Large Language Models for Decision Support in Personalized Oncology

Clinical interpretation of complex biomarkers for precision oncology currently requires manual investigations of previous studies and databases. Conversational large language models (LLMs) might be beneficial as automated tools for assisting clinical decision-making. To assess performance and define their role using 4 recent LLMs as support tools for precision oncology. This diagnostic study examined 10 fictional cases of patients with advanced cancer with genetic alterations. Each case was submitted to 4 different LLMs (ChatGPT, Galactica, Perplexity, and BioMedLM) and 1 expert physician to identify personalized treatment options in 2023. Treatment options were masked and presented to a molecular tumor board (MTB), whose members rated the likelihood of a treatment option coming from an LLM on a scale from 0 to 10 (0, extremely unlikely; 10, extremely likely) and decided whether the treatment option was clinically useful. Number of treatment options, precision, recall, F1 score of LLMs compared with human experts, recognizability, and usefulness of recommendations. For 10 fictional cancer patients (4 with lung cancer, 6 with other; median [IQR] 3.5 [3.0-4.8] molecular alterations per patient), a median (IQR) number of 4.0 (4.0-4.0) compared with 3.0 (3.0-5.0), 7.5 (4.3-9.8), 11.5 (7.8-13.0), and 13.0 (11.3-21.5) treatment options each was identified by the human expert and 4 LLMs, respectively. When considering the expert as a criterion standard, LLM-proposed treatment options reached F1 scores of 0.04, 0.17, 0.14, and 0.19 across all patients combined. Combining treatment options from different LLMs allowed a precision of 0.29 and a recall of 0.29 for an F1 score of 0.29. LLM-generated treatment options were recognized as AI-generated with a median (IQR) 7.5 (5.3-9.0) points in contrast to 2.0 (1.0-3.0) points for manually annotated cases. A crucial reason for identifying AI-generated treatment options was insufficient accompanying evidence. For each patient, at least 1 LLM generated a treatment option that was considered helpful by MTB members. Two unique useful treatment options (including 1 unique treatment strategy) were identified only by LLM. In this diagnostic study, treatment options of LLMs in precision oncology did not reach the quality and credibility of human experts; however, they generated helpful ideas that might have complemented established procedures. Considering technological progress, LLMs could play an increasingly important role in assisting with screening and selecting relevant biomedical literature to support evidence-based, personalized treatment decisions.

Implementation strategies for large scale quality improvement initiatives in primary care settings: a qualitative assessment

Background and objectivesThe EvidenceNOW: Advancing Heart Health in Primary Care was designed to assist primary care practices in the US in implementing evidence-based practices in cardiovascular care and building capacity for quality improvement. EvidenceNOW, NCT03054090, was registered with ClinicalTrials.gov on 15/02/2017. The goals of this study were to gain a comprehensive understanding of perspectives from research participants and research team members on the value of implementation strategies and factors that influenced the EvidenceNOW initiative in Virginia.MethodsIn 2018, we conducted 25 focus groups with clinicians and staff at participating practices, including 80 physicians, advanced practice clinicians, practice managers and other practice staff. We also conducted face-to-face and telephone interviews with 22 research team members, including lead investigators, practice facilitators, physician expert consultants, and evaluators. We used the integrated-Promoting Action on Research Implementation in the Health Services (i-PARIHS) framework in our qualitative data analysis and organization of themes.ResultsImplementation strategies valued by both practice representatives and research team members included the kick-off event, on-site practice facilitation, and interaction with physician expert consultants. Remote practice facilitation and web-based tools were used less frequently. Contextual factors that influence quality improvement efforts include leadership support, access to resources, previous quality improvement experience, and practice ownership type (independent compared to health system owned). Many clinicians and staff were overwhelmed by day-to-day activities and experience initiative fatigue, which hindered their ability to fully participate in the EvidenceNOW initiative.ConclusionsThis study provides details on how the practice environment plays an essential role in the implementation of evidence-based practices in primary care. Future efforts to improve quality in primary care practices should consider the context and environment of individual practices, with targeted implementation strategies to meet the needs of independent and health system owned practices. Future efforts to improve quality in primary care practices require strategies to address initiative fatigue among clinicians and practice staff. External support for building capacity for quality improvement could help primary care practices implement and sustain evidence-based practices and improve quality of care.Trial registrationThis project was registered with ClinicalTrials.gov on 15/02/2017 and the identifier is NCT03054090.

Application of the Face2Gene tool in an Italian dysmorphological pediatric clinic: Retrospective validation and future perspectives.

Neurodevelopmental disorders exhibit recurrent facial features that can suggest the genetic diagnosis at a glance, but recognizing subtle dysmorphisms is a specialized skill that requires very long training. Face2Gene (FDNA Inc) is an innovative computer-aided phenotyping tool that analyses patient's portraits and suggests 30 candidate syndromes with similar morphology in a prioritized list. We hypothesized that the software could support even expert physicians in the diagnostic workup of genetic conditions. In this study, we assessed the performance of Face2Gene in an Italian dysmorphological pediatrics clinic. We uploaded two-dimensional face pictures of 145 children affected by genetic conditions with typical phenotypic traits. All diagnoses were previously confirmed by cytogenetic or molecular tests. Overall, the software's differential included the correct syndrome in most cases (98%). We evaluated the efficiency of the algorithm even considering the rareness of the genetic conditions. All "common" diagnoses were correctly identified, most of them with high diagnostic accuracy (93% in top-3 matches). Finally, the performance for the most common pediatric syndromes was calculated. Face2Gene performed well even for ultra-rare genetic conditions (75% within top-3 matches and 83% within top-10 matches). Expert geneticists maybe do not need computer support to recognize common syndromes, but our results prove that the tool can be useful not only for general pediatricians but also in dysmorphological clinics for ultra-rare genetic conditions.

Evaluating the Performance of Large Language Models in Hematopoietic Stem Cell Transplantation Decision Making

Introduction: Large Language Models (LLMs) are a form of Artificial Intelligence (AI), by identifying patterns and connections within data, they can predict the most likely words or phrases in specific contexts. Previous studies have indicated that GPT (Generative Pre-trained Transformer; OpenAI) performs well in answering single-choice clinical questions. However, its performance seems to be less satisfactory when dealing with multiple-choice questions and more intricate clinical cases (Cosima et al. 2023 EAO; Cascella et al. 2023 J Med Syst). Notably, no study has evaluated LLMs responses in the context of Transplantation Decision Making, a complex process heavily reliant on physician expertise. Additionally, most studies focused solely on GPT's performance, without considering other competitive LLMs like Llama-2 or VertexAI. Our study aims to assess the performance of LLMs in the domain of hematopoietic stem cell transplantation. Methods: We modified and anonymized the clinical histories of six hematological patients. An experienced hematologist reviewed and validated these modified clinical histories, which included demographic data, past medical history, hematology disease features (genetic data and MRD when available), treatment responses, adverse events from previous therapies, and potential donor information (related/unrelated, HLA, CMV status). We presented these clinical cases to six experienced bone marrow transplant physicians from two major JACIE accredited hospitals and 11 hematology residents from the University Milano-Bicocca. LLMs employed for the analysis were: GPT-4, VertexAI Palm 2, Llama-2 13b and 70b. LLMs were configured with different temperature settings to control token selection randomness, always maintaining low levels for more deterministic responses. A triple-blinded survey was conducted using Typeform, where both senior hematologists and residents provided anonymized responses with personal tokens. The senior hematologists, residents, and LLMs testers were unaware of the responses provided by the other groups. We calculated Fleiss K (K) and overall percentage of agreement (OA) between residents and LLMs, considering the consensus answer (CoA) among experts as the most frequent response. Subsequently, OA and K values for both residents and LLMs were compared using T- or Mann-Whitney tests with Graphpad v 10.0.1. Results: The results showed perfect agreement among experts in patient transplant eligibility assessment (K=1.0) and substantial agreement in the choice of donors and conditioning regimens (K=0.62 for both questions). Fair agreement was observed in Transplant Related Mortality (TRM) estimation (K=0.22). The median OA and K value between residents and the CoA of experts were 76.5% (range 52.9-88.2%) and 0.61 (range 0.4-0.8), respectively. The median OA and K value between LLMs answers and experts were 58.8% (range 47-71%) and 0.45 (range 0.3-0.61), respectively. The mean OA and K value of residents were significantly higher compared to LLMs (p=0.02). Specifically, residents showed higher median OA and K values in patient eligibility assessment (median OA 100 vs. 83% and K 1 vs. 0.78; p=0.01). However, there was no significant difference in median K for donor choice (0.56 vs. 0.56), conditioning regimen (0.67 vs. 0.33), and TRM evaluation (0.33 vs. 0) (Table 1). The median K values of GPT-4, Palm-2, Llama2-13b, and Llama2-70b were 0.49, 0.53, 0.33, and 0.53 respectively (Figure 1). Conclusion: Our study sheds light on the potential and limitations of LLMs in complex hematopoietic stem cell transplantation decision-making. While LLMs showed promising results with a median OA of 59%, residents demonstrated superior performance. LLMS displayed good performances in patients' eligibility and donor choice but showed shortcomings in conditioning regimens and TRM evaluation. Not using a rating scale from experts when evaluating LLMs responses aimed to avoid potential bias. However, it is important to note that the consensus answer, even though it was the most frequent, does not necessarily imply that other responses provided by the experts were incorrect. Therefore, the lower consensus among the experts in TRM evaluation, possibly due to the challenge of precisely calculating TRM in a survey-based evaluation, should also lead to a cautious approach when evaluating residents and LLMs answers in this setting.

AHOD2131: A Randomized Phase 3 Response-Adapted Trial Comparing Standard Therapy with Immuno-Oncology Therapy for Children and Adults with Newly Diagnosed Stage I and II Classic Hodgkin Lymphoma

Background: Chemotherapy in combination with radiotherapy (RT) is the standard for early-stage (ES) Hodgkin lymphoma (cHL). Despite high cure rates (12-year overall survival (OS) >93%), there is room to improve short and long-term outcomes for children and adults with ES cHL. Incorporation of immunotherapy (IO) into front line treatment of ES cHL is an opportunity to both improve progression-free survival (PFS) and maintain OS, while minimizing long-term morbidity and mortality by reducing exposure to RT and high-dose chemotherapy. Study Design and Methods: HL leaders of the NCTN groups, expert HL researchers and physicians, and representatives from the Cancer Therapy Evaluation Program (CTEP) formed a working group in April 2020 with the goals of harmonizing treatment approaches for ES cHL across pediatric and adult patients and reaching consensus regarding the optimal study design for incorporating IO therapy into frontline treatment. Study champions identified from each of the North American cooperative groups [Children's Oncology Group (COG), SWOG, ECOG-ACRIN, Alliance, NRG] and experts in imaging, radiation oncology, lymphoma biology and patient-reported outcomes (PROs) were included. The resulting COG-led clinical trial, AHOD2131, represents the largest ES cHL trial in the history of North American cooperative groups and the first to enroll patients across the age continuum. AHOD2131 (NCT05675410; Figure) is a randomized, phase 3 trial enrolling patients ages 5 to 60 years with newly diagnosed, previously untreated stage I and II cHL and will investigate the addition of the CD30-antibody drug conjugate brentuximab vedotin (Bv) with PD-1 blockade (nivolumab) compared to standard chemotherapy +/- RT. Since the April 2023 activation, 97 North American sites have activated the trial, and 21 participants have enrolled. Target enrollment is 1875 patients over 5 years of accrual, for an estimated 1782 eligible and evaluable patients. The primary objective is to compare the 3-year PFS of patients with ES cHL treated through a response-adapted, superiority design with either standard therapy or with an IO approach (BV + nivolumab). All patients will be stratified as favorable or unfavorable based on disease risk features at enrollment. Based on initial response assessment by PET/CT (via central review) after 2 cycles of ABVD, patients were classified to PET2 positive (SER, defined as 5 Point Score 4 or 5, ~15% of patients) and PET negative (RER). Those who are PET positive will receive involved site RT (ISRT). The SER and RER patients will be randomized to standard chemotherapy vs. IO therapy respectively. In addition to the primary outcome of PFS, there are 11 secondary and 10 exploratory aims. The secondary aims focus on OS, PFS of subgroups, and event-free survival at 12 years, physician-reported treatment-related adverse event (AE) rates, patient-reported AE using pediatric and adult versions of Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE), and the capture of representation by self-report and the use of electronic PRO data collections. The exploratory aims include the assessment of imaging central review as compared to local institutional review and the use of convolutional neural networks through artificial-intelligence machine learning. The study will examine the effect of therapy on acute and long-term adverse effects including health-related quality of life (HRQoL) and OS for up to 12 years. The study will compare patient self-report and provider AE reports and evaluate the associations of self-reported race/ethnicity and social determinants of health (SDOH)and post-progression/post-relapse OS. AHOD2131 strengthens the effort between the North American cooperative groups to conduct collaborative clinical trials and aims to harmonize an improved standard of care for ES cHL across the age continuum.

The Significance of a “MGUS” Tumor Board

Our large academic medical center consists of both a main campus and several regional sites that all offer hematology/oncology services where monoclonal gammopathy cases are referred for consultation. We receive well over 100 requests annually to see cases with abnormal monoclonal proteins. The challenge was how to triage these requests, with the goal of improving patient access to main campus myeloma experts while improving patient care and timely management. We decided to schedule cases initially referred for monoclonal gammopathy of undetermined significance (MGUS) with our advanced practice providers (APPs) both in the region and at main campus. Due to the concern for varying experience managing MGUS, differences in diagnostic practice and limited guidance we developed a novel concept to have a tumor board to review MGUS cases that were initially seen by our APPs. The “MGUS” tumor board is ran by three of our main campus myeloma focused hematologists and our myeloma dedicated APRN-PhD. We meet twice a month to review cases. Over the past 2 years we reviewed 147 cases providing recommendations for further work up. Of these, 41 cases (28%) were found to have a clinically significant monoclonal protein necessitating referral to a physician at main campus. The breakdown of these cases are detailed below in T able 1. Sixteen patients (11%) were started on treatment, the other 25 (17%) remained on observation. Another aspect of the tumor board is to provide education regarding clinically significant scenarios that involve monoclonal proteins. This tumor board offers CME credit for both APPs and physicians. By developing a team of knowledgeable APPs we have shifted 72% of monoclonal gammopathy referrals to remain under the care of APPs which improved access to main campus myeloma physician experts. This allowed the myeloma team at main campus to see more second opinions, open more clinical trials and expand our cell-therapy options for patients. Our referral base has grown and we have hired more APPs in the region and two additional myeloma physicians at main campus since starting the “MGUS” tumor board. Overall, we were successful in transitioning “MGUS” referrals to our APPs, but there were higher than expected percent of patients with clinically significant monoclonal proteins. Our data shows that it is essential for there to be some standardization for reviewing and working up “MGUS” referrals. This is especially important at large centers with multiple regional locations. We recommend a tumor board style review as it allows for discussion and education of “MGUS” referrals which prove to be far more complex in many cases.

The utility of word list and story recall for identifying older U.S. Chinese immigrants with cognitive impairment.

This study examined the utility of the Chinese-language translations of the word list memory test (Philadelphia Verbal Learning Test) and story memory test (Logical Memory subtest of the Wechsler Memory Scale) for differentiating cognitive diagnosis in older U.S. Chinese immigrants. Participants were ≥ 60 years old, with Chinese language proficiency to complete a diagnostic workup at the Mount Sinai's Alzheimer's Disease Research Center. The workup included an evaluation by a geriatric psychiatrist and cognitive testing with a psychometrician. Diagnosis of normal, mild cognitive impairment (MCI), and dementia was made independent of the cognitive tests at consensus led by a dementia expert physician. Multivariable logistic regression models were used to assess the sensitivity of story and word list memory tests for distinguishing between groups. Receiver operating characteristic (ROC area/area under the curve [AUC]) was used to compare the predictive accuracy of the two tests. The sample included 71 participants with normal cognition, 42 with MCI, and 24 with dementia. The MCI group was older and less educated than normal controls but younger and more educated than the dementia group. Delayed recall of both memory tests, but not immediate recall of either test, predicted diagnosis. While composite memory score of word list (AUC = 0.90) predicted diagnosis slightly better than that of stories (AUC = 0.85), the difference was not significant in this small sample (p = .14). Chinese-language translations of verbal memory tests, in particular delayed recall scores, were equally sensitive for classifying cognitive diagnosis in older U.S. Chinese immigrants. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Physician views of artificial intelligence in otolaryngology and rhinology: A mixed methods study.

The study aimed to investigate otolaryngologists' knowledge, trust, acceptance, and concerns with clinical applications of artificial intelligence (AI). This study used mixed methods with survey and semistructured interviews. Survey was e-mailed to American Rhinologic Society members, of which a volunteer sample of 86 members responded. Nineteen otolaryngologists were purposefully recruited and interviewed until thematic saturation was achieved. Seventy-six respondents (10% response rate) completed the majority of the survey: 49% worked in academic settings and 43% completed residency 10 or fewer years ago. Of 19 interviewees, 58% worked in academic settings, and 47% completed residency 10 or fewer years ago. Familiarity: Only 8% of survey respondents reported having AI training in residency, although 72% had familiarity with general AI concepts; 0 interviewees had personal experience with AI in clinical settings. Expected uses: Of the surveyed otolaryngologists, 82% would use an AI-based clinical decision aid and 74% were comfortable with AI proposing treatment recommendations. However, only 44% of participants would trust AI to identify malignancy and 53% to interpret radiographic images. Interviewees trusted AI for simple tasks, such as labeling septal deviation, more than complex ones, such as identifying tumors. Factors influencing AI adoption: 89% of survey participants would use AI if it improved patient satisfaction, 78% would be willing to use AI if experts and studies validated the technologies, and 73% would only use AI if it increased efficiency. Sixty-one percent of survey respondents expected AI incorporation into clinical practice within 5 years. Interviewees emphasized that AI adoption depends on its similarity to their clinical judgment and to expert opinion. Concerns included nuanced or complex cases, poor design or accuracy, and the personal nature of physician-patient relationships. Few physicians have experience with AI technologies but expect rapid adoption in the clinic, highlighting the urgent need for clinical education and research. Otolaryngologists are most receptive to AI "augmenting" physician expertise and administrative capacity, with respect for physician autonomy and maintaining relationships with patients. Level VI, descriptive or qualitative study.

SDA-Net: Self-distillation driven deformable attentive aggregation network for thyroid nodule identification in ultrasound images

Early detection and accurate identification of thyroid nodules are the major challenges in controlling and treating thyroid cancer that can be difficult even for expert physicians. Currently, many computer-aided diagnosis (CAD) systems have been developed to assist this clinical process. However, most of these systems are unable to well capture geometrically diverse thyroid nodule representations from ultrasound images with subtle and various characteristic differences, resulting in suboptimal diagnosis and lack of clinical interpretability, which may affect their credibility in the clinic. In this context, a novel end-to-end network equipped with a deformable attention network and a distillation-driven interaction aggregation module (DIAM) is developed for thyroid nodule identification. The deformable attention network learns to identify discriminative features of nodules under the guidance of the deformable attention module (DAM) and an online class activation mapping (CAM) mechanism and suggests the location of diagnostic features to provide interpretable predictions. DIAM is designed to take advantage of the complementarities of adjacent layers, thus enhancing the representation capabilities of aggregated features; driven by an efficient self-distillation mechanism, the identification process is complemented with more multi-scale semantic information to calibrate the diagnosis results. Experimental results on a large dataset with varying nodule appearances show that the proposed network can achieve competitive performance in nodule diagnosis and provide interpretability suitable for clinical needs.

Large language models propagate race-based medicine

Large language models (LLMs) are being integrated into healthcare systems; but these models may recapitulate harmful, race-based medicine. The objective of this study is to assess whether four commercially available large language models (LLMs) propagate harmful, inaccurate, race-based content when responding to eight different scenarios that check for race-based medicine or widespread misconceptions around race. Questions were derived from discussions among four physician experts and prior work on race-based medical misconceptions believed by medical trainees. We assessed four large language models with nine different questions that were interrogated five times each with a total of 45 responses per model. All models had examples of perpetuating race-based medicine in their responses. Models were not always consistent in their responses when asked the same question repeatedly. LLMs are being proposed for use in the healthcare setting, with some models already connecting to electronic health record systems. However, this study shows that based on our findings, these LLMs could potentially cause harm by perpetuating debunked, racist ideas.

Combining Physician Expertise and Women's Lived Experience to Educate Health Professionals about Preventing Fetal Alcohol Spectrum Disorders.

Physician Champions from the American College of Obstetricians and Gynecologists (ACOG) and trained women Speakers from FASD United, who have given birth to a child with a fetal alcohol spectrum disorder (FASD), co-present to healthcare providers (HCPs) in medical residency programs as part of an educational intervention. They present FASDs as a biological and social problem surrounded by stigma that prevent pregnant women from talking openly to their HCPs about their alcohol use or alcohol use disorder (AUD) and getting the medical help they need. Semi-structured interviews were conducted with 10 ACOG Champions and nine FASD United Speakers and a thematic analysis assessed how the co-presentations can enhance HCPs' understanding about FASDs and address stigma associated with alcohol use during pregnancy. Interview findings indicated that both Champions and Speakers emphasized the need for HCPs to be nonjudgmental and create a safe space for open dialogue. They reported that residents were moved by mothers' personal stories, wanted to understand AUD better, and asked about the type of help HCPs can offer women. Combining physicians' expertise with mothers' personal stories of lived experiences of FASDs directed at residents, who are more reflective and open at this phase of their careers, moved them from a fact-based to an empathy-based approach to learning that is critical to address the stigma surrounding women who may be using alcohol or struggling with an AUD during pregnancy. Collaboration between national organizations allowed this intervention to be widely implemented across the country.

Determination and prioritization of factors affecting the occurrence of needle stick injuries among healthcare workers using techniques of Delphi and fuzzy analytical hierarchy process (FAHP)

IntroductionNeedlestick injuries (NSIs) are a major hazard in the workplace for healthcare workers. To prevent these injuries, it is essential to determine the important factors affecting the occurrence of them. This study aimed to identify, classify and prioritize these factors using techniques of Delphi and fuzzy analytical hierarchy process (FAHP).MethodsThis descriptive-analytical study was conducted in 2022. Firstly, the factors affecting the occurrence of needlestick injuries were identified by the literature review. Moreover, the Delphi technique was used to identify the factors. 20 experts (physicians, nurses, and occupational health experts) participated in the steps of the Delphi method. Then, these factors were grouped into six groups. In the next step, the fuzzy analytical hierarchy process (FAHP) was applied to prioritize the factors. For this purpose, the pairwise comparison questionnaire was designed and filled out by 20 experts. Finally, data were analyzed using MATLAB software (version 2018a).Results42 factors (31 factors extracted from the literature review and 11 factors obtained from the Delphi technique) were identified in this study. These factors were categorized into six groups. Based on the results, the relative weight of non-demographic personal factors, tool and technology factors, job factors, organizational factors, demographic personal factors, and environmental factors were computed by 0.200, 0.185, 0.184, 0.157, 0.142, and 0.133, respectively.ConclusionThese results determined the importance of the factors affecting the occurrence of needlestick injuries. These findings can be useful for planning preventive measures.

Molecular tumour boards - current and future considerations for precision oncology.

Over the past 15 years, rapid progress has been made in developmental therapeutics, especially regarding the use of matched targeted therapies against specific oncogenic molecular alterations across cancer types. Molecular tumour boards (MTBs) are panels of expert physicians, scientists, health-care providers and patient advocates who review and interpret molecular-profiling results for individual patients with cancer and match each patient to available therapies, which can include investigational drugs. Interpretation of the molecular alterations found in each patient is a complicated task that requires an understanding of their contextual functional effects and their correlations with sensitivity or resistance to specific treatments. The criteria for determining the actionability of molecular alterations and selecting matched treatments are constantly evolving. Therefore, MTBs have an increasingly necessary role in optimizing the allocation of biomarker-directed therapies and the implementation of precision oncology. Ultimately, increased MTB availability, accessibility and performance are likely to improve patient care. The challenges faced by MTBs are increasing, owing to the plethora of identifiable molecular alterations and immune markers in tumours of individual patients and their evolving clinical significance as more and more data on patient outcomes and results from clinical trials become available. Beyond next-generation sequencing, broader biomarker analyses can provide useful information. However, greater funding, resources and expertise are needed to ensure the sustainability of MTBs and expand their outreach to underserved populations. Harmonization between practice and policy will be required to optimally implement precision oncology. Herein, we discuss the evolving role of MTBs and current and future considerations for their use in precision oncology.

Development and Validation of a Novel Mobile Application for Bone Age Assessment

Background: Despite its pivotal role, the use of bone age is limited due to a lack of physician expertise, time, and access. Objective: To develop and validate a mobile application-based bone age assessment tool. Study Design: The study involved the selection of standardized images (from 307 radiographs), delineation (90 radiographs), evaluation (200 radiographs) and incorporation of regions of interest on the application, and validation against the Tanner-Whitehouse 3 method by an expert (252 radiographs) and non-expert users (110 radiographs). Results: The application-based assessment by expert users had an absolute standardized difference of 4.7 months (95% confidence interval; 4.2-5.2 months), a relative standardized difference deviation of 4.4% (3.9-4.9%), and similar intraindividual [2.8 (1.9-3.7) months versus 3.6 (2.8-4.5) months] and interindividual variation [4.2 (3.3-5.0) versus 4.2 (3.3-5.2) months] compared to the TW3 method. Non-expert user assessment had an absolute standardized difference of 6.7 months (5.6-7.7 months) and a relative standardized difference deviation of 5.8% (4.7-6.9%). The mean test time was lower for application than TW3 for both expert [1.3 (0.2) versus 3.1 (1.0) minutes, p<0.001] and non-expert users [2.8 (1.0) versus 5.0 (1.4) minutes, p<0.001]. Discussion: Our study confirms the accuracy of mobile application-based bone age assessment. This, along with good precision, reduced complexity, and lower time requirement, suggests a potential for its widespread implementation.

Editor's Choice – Management of Lower Extremity Venous Outflow Obstruction: Results of an International Delphi Consensus

The endovascular treatment of venous obstruction has expanded significantly in recent years. Best practices for optimal patient outcomes are not well established and the evidence base is poor. The purpose of this study was to obtain consensus on management criteria for patients with lower extremity venous outflow obstruction. The study was conducted as a two round Delphi consensus. Statements addressed imaging, symptoms and other baseline measures, differential diagnosis, treatment algorithm, indications for stenting, inflow and outflow assessment, successful procedural outcomes, post-procedure therapies and stent surveillance, and clinical success factors. Statements were prepared by six expert physicians (round 1, 40 statements) and an expanded panel of 24 physicians (round 2, 80 statements) and sent to a pre-identified group of venous experts who met qualifying criteria. A 9 point Likert scale was used and consensus was defined as ≥ 70% of respondents rating a statement between 7 and 9 (agreement) or between 1 and 3 (disagreement). Round 1 results were used to guide rewording and splitting compound statements for greater clarity in round 2. In round 1, 75 of 110 (68%) experts responded, and 91 of 121 (75%) experts responded in round 2. Round 1 achieved consensus in 32/40 (80%) statements. Consensus was not reached in the treatment algorithm section. Round 2 achieved consensus in 50/80 (62.5%). Statements reaching consensus were imaging (2/3, 66%), symptoms and other baseline measures (12/24, 50%), differential diagnosis (2/8, 25%), treatment algorithm (10/17, 59%), indications for stenting (10/10, 100%), inflow and outflow assessment (2/2, 100%), procedural outcomes (2/2, 100%), post-procedure therapies and stent surveillance, (5/7, 71%), and clinical success factors (5/7, 71%). This study demonstrated that considerable consensus was achieved between venous experts on the optimal management of lower extremity venous outflow obstruction. There were multiple domains where consensus is lacking, highlighting important areas for further investigation and research.

An Actionable Expert-System Algorithm to Support Nurse-Led Cancer Survivorship Care: Algorithm Development Study.

Comprehensive models of survivorship care are necessary to improve access to and coordination of care. New models of care provide the opportunity to address the complexity of physical and psychosocial problems and long-term health needs experienced by patients following cancer treatment. This paper presents our expert-informed, rules-based survivorship algorithm to build a nurse-led model of survivorship care to support men living with prostate cancer (PCa). The algorithm is called No Evidence of Disease (Ned) and supports timelier decision-making, enhanced safety, and continuity of care. An initial rule set was developed and refined through working groups with clinical experts across Canada (eg, nurse experts, physician experts, and scientists; n=20), and patient partners (n=3). Algorithm priorities were defined through a multidisciplinary consensus meeting with clinical nurse specialists, nurse scientists, nurse practitioners, urologic oncologists, urologists, and radiation oncologists (n=17). The system was refined and validated using the nominal group technique. Four levels of alert classification were established, initiated by responses on the Expanded Prostate Cancer Index Composite for Clinical Practice survey, and mediated by changes in minimal clinically important different alert thresholds, alert history, and clinical urgency with patient autonomy influencing clinical acuity. Patient autonomy was supported through tailored education as a first line of response, and alert escalation depending on a patient-initiated request for a nurse consultation. The Ned algorithm is positioned to facilitate PCa nurse-led care models with a high nurse-to-patient ratio. This novel expert-informed PCa survivorship care algorithm contains a defined escalation pathway for clinically urgent symptoms while honoring patient preference. Though further validation is required through a pragmatic trial, we anticipate the Ned algorithm will support timelier decision-making and enhance continuity of care through the automation of more frequent automated checkpoints, while empowering patients to self-manage their symptoms more effectively than standard care. RR2-10.1136/bmjopen-2020-045806.