List Of Differential Diagnoses Research Articles

Facilitating Trust Calibration in Artificial Intelligence-Driven Diagnostic Decision Support Systems for Determining Physicians' Diagnostic Accuracy: Quasi-Experimental Study.

Diagnostic errors are significant problems in medical care. Despite the usefulness of artificial intelligence (AI)-based diagnostic decision support systems, the overreliance of physicians on AI-generated diagnoses may lead to diagnostic errors. We investigated the safe use of AI-based diagnostic decision support systems with trust calibration by adjusting trust levels to match the actual reliability of AI. A quasi-experimental study was conducted at Dokkyo Medical University, Japan, with physicians allocated (1:1) to the intervention and control groups. A total of 20 clinical cases were created based on the medical histories recorded by an AI-driven automated medical history-taking system from actual patients who visited a community-based hospital in Japan. The participants reviewed the medical histories of 20 clinical cases generated by an AI-driven automated medical history-taking system with an AI-generated list of 10 differential diagnoses and provided 1 to 3 possible diagnoses. Physicians were asked whether the final diagnosis was in the AI-generated list of 10 differential diagnoses in the intervention group, which served as the trust calibration. We analyzed the diagnostic accuracy of physicians and the correctness of the trust calibration in the intervention group. We also investigated the relationship between the accuracy of the trust calibration and the diagnostic accuracy of physicians, and the physicians' confidence level regarding the use of AI. Among the 20 physicians assigned to the intervention (n=10) and control (n=10) groups, the mean age was 30.9 (SD 3.9) years and 31.7 (SD 4.2) years, the proportion of men was 80% and 60%, and the mean postgraduate year was 5.8 (SD 2.9) and 7.2 (SD 4.6), respectively, with no significant differences. The physicians' diagnostic accuracy was 41.5% in the intervention group and 46% in the control group, with no significant difference (95% CI -0.75 to 2.55; P=.27). The overall accuracy of the trust calibration was only 61.5%, and despite correct calibration, the diagnostic accuracy was 54.5%. In the multivariate logistic regression model, the accuracy of the trust calibration was a significant contributor to the diagnostic accuracy of physicians (adjusted odds ratio 5.90, 95% CI 2.93-12.46; P<.001). The mean confidence level for AI was 72.5% in the intervention group and 45% in the control group, with no significant difference. Trust calibration did not significantly improve physicians' diagnostic accuracy when considering the differential diagnoses generated by reading medical histories and the possible differential diagnosis lists of an AI-driven automated medical history-taking system. As this was a formative study, the small sample size and suboptimal trust calibration methods may have contributed to the lack of significant differences. This study highlights the need for a larger sample size and the implementation of supportive measures of trust calibration.

Bone Tumors of the Jaw - the "Blind Spot" for Radiologists Experienced with Tumors? - Part II.

Primary bone tumours of the jaw are rare tumoral entities and do substantially differ from other bone tumours of the human body with respect of their frequently encountered unusual radiological appearances. The reason for that may be confined to the co-existence of two closely neighbored but different anatomical structures (i.e., tooth-forming apparatus and jaw bones with adjacent gingiva) and some tumour pathologies which are nearly excusively encountered in the jaw bones only (e.g., ameloblastoma, ossifying fibroma, ghost cell carcinoma).This paper would like to highlight some basic principles of the diagnostic approach and possibilities of radiological differentiation of such tumour-suspicious changes within the gnathic system are elucidated and discussed.The paper presented here is substantially based on the most recent classification of odontogenic and maxillofacial tumours (5th edition, 2022) which serves as a scaffold for the selection of typical tumour entities. Due to the educational character of this paper, only important jaw tumours worth mentioning and their characteristics are subject to be extracted from the literature and further discussed.The main focus was put onto both the description of radiological tumoral appearance and the rational selection of a radiological diagnostic work-up. In order to better visualize this difficult field of tumour entities, much attention has been paid on a comprehensive pictorial essay.For radiologists, it is their foremast task to detect, describe, and to classify bone tumours of the jaw when they are found intentionally or accidentally, resp. A close co-operation with their clinical partners is of upmost importance to gain information about patient´s history and clinical presentation. It is readily reasonable that radiologists are mostly able to provide only a suggestion of the presented tumour entity but this expert opinion would be very helpful to further narrow down the list of potential differential diagnoses (e.g., differentiation of a cyst vs. solid tumour osteolysis, identification of jaw osteomyelitis vs. tumoral infiltration, recognizing of secondary tumour involvement of the jaw). · primary bone tumours of the jaw are very rare, moreover difficult to differentiate radiologically, and do need therefore histological proof;. · profound knowledge about tumour characteristics (location within the jaw, relationship to the tooth, bony destructive pattern) may allow a rough orientation and classification;. · matrix-forming tumours and dysplasias of the jaw facilitates their radiological differentiation and classification;. · in contrary, osteolyses should be thoroughly scrutinized for the more frequent gnathic cysts in differentiation of rather rare solid primary tumours;. · an interdisciplinary round-table discussion amongst well-experienced maxillofacial surgeons and specialized radiologists may be appropriate to avoid severe misinterpretations.. · Grieser T, Hirsch E, Tödtmann N. Tumours of the jaw bones - Part II. Fortschr Röntgenstr 2024; DOI 10.1055/a-2416-1282.

Bone Tumors of the Jaw - the "Blind Spot" for Radiologists Experienced with Tumors? - Part I.

Primary bone tumours of the jaw are rare tumoral entities and do substantially differ from other bone tumours of the human body with respect of their frequently encountered unusual radiological appearances. The reason for that may be confined to the co-existence of two closely neighbored but different anatomical structures (i.e., tooth-forming apparatus and jaw bones with adjacent gingiva) and some tumour pathologies which are nearly excusively encountered in the jaw bones only (e.g., ameloblastoma, ossifying fibroma, ghost cell carcinoma).This paper would like to highlight some basic principles of the diagnostic approach and possibilities of radiological differentiation of such tumour-suspicious changes within the gnathic system are elucidated and discussed.The paper presented here is substantially based on the most recent classification of odontogenic and maxillofacial tumours (5th edition, 2022) which serves as a scaffold for the selection of typical tumour entities. Due to the educational character of this paper, only important jaw tumours worth mentioning and their characteristics are subject to be extracted from the literature and further discussed.The main focus was put onto both the description of radiological tumoral appearance and the rational selection of a radiological diagnostic work-up. In order to better visualize this difficult field of tumour entities, much attention has been paid on a comprehensive pictorial essay.For radiologists, it is their foremast task to detect, describe, and to classify bone tumours of the jaw when they are found intentionally or accidentally, resp. A close co-operation with their clinical partners is of upmost importance to gain information about patient's history and clinical presentation. It is readily reasonable that radiologists are mostly able to provide only a suggestion of the presented tumour entity but this expert opinion would be very helpful to further narrow down the list of potential differential diagnoses (e.g., differentiation of a cyst vs. solid tumour osteolysis, identification of jaw osteomyelitis vs. tumoral infiltration, recognizing of secondary tumour involvement of the jaw). · primary bone tumours of the jaw are very rare, moreover difficult to differentiate radiologically, and do need therefore histological proof;. · profound knowledge about tumour characteristics (location within the jaw, relationship to the tooth, bony destructive pattern) may allow a rough orientation and classification;. · matrix-forming tumours and dysplasias of the jaw facilitates their radiological differentiation and classification;. · in contrary, osteolyses should be thoroughly scrutinized for the more frequent gnathic cysts in differentiation of rather rare solid primary tumours;. · an interdisciplinary round-table discussion amongst well-experienced maxillofacial surgeons and specialized radiologists may be appropriate to avoid severe misinterpretations.. · Grieser T, Hirsch E, Tödtmann N. Bone Tumors of the Jaw - the "Blind Spot" for Radiologists Experienced with Tumors? - Part I. Fortschr Röntgenstr 2024; DOI 10.1055/a-2415-8880.

Tumour mimics in paediatric neuroimaging.

Distinguishing tumours from other conditions is a primary challenge in paediatric neuro-radiology. This paper aims to describe mimics, which are non-neoplastic conditions that have features similar to a neoplastic process caused by a non-neoplastic entity, and chameleons, which are uncommon presentations of brain tumours that are mistaken for other diagnoses. By doing so, we aim to raise awareness of these conditions and prevent inappropriate investigations or treatment in children. When suspecting a brain tumour, a detailed history, physical examination, and appropriate laboratory investigations can provide important clues about the nature of the lesion and narrow the list of possible differential diagnoses. Presented here is a collection of cases that have puzzled us for various reasons, including the absence of symptoms, coincidental timing, or misleading radiological features. Included in this pictorial essay are cases in which only a biopsy has helped us to make the correct diagnosis, as well as cases in which an unsuccessful biopsy has allowed us to evaluate hypotheses that were previously unaddressed. The paper also highlights the limited knowledge we have about the intercausality between malformations and later onset tumours, and the spectrum of manifestations that metabolic and genetic disorders can have.

Comparative Analysis of Diagnostic Performance: Differential Diagnosis Lists by LLaMA3 Versus LLaMA2 for Case Reports.

Generative artificial intelligence (AI), particularly in the form of large language models, has rapidly developed. The LLaMA series are popular and recently updated from LLaMA2 to LLaMA3. However, the impacts of the update on diagnostic performance have not been well documented. We conducted a comparative evaluation of the diagnostic performance in differential diagnosis lists generated by LLaMA3 and LLaMA2 for case reports. We analyzed case reports published in the American Journal of Case Reports from 2022 to 2023. After excluding nondiagnostic and pediatric cases, we input the remaining cases into LLaMA3 and LLaMA2 using the same prompt and the same adjustable parameters. Diagnostic performance was defined by whether the differential diagnosis lists included the final diagnosis. Multiple physicians independently evaluated whether the final diagnosis was included in the top 10 differentials generated by LLaMA3 and LLaMA2. In our comparative evaluation of the diagnostic performance between LLaMA3 and LLaMA2, we analyzed differential diagnosis lists for 392 case reports. The final diagnosis was included in the top 10 differentials generated by LLaMA3 in 79.6% (312/392) of the cases, compared to 49.7% (195/392) for LLaMA2, indicating a statistically significant improvement (P<.001). Additionally, LLaMA3 showed higher performance in including the final diagnosis in the top 5 differentials, observed in 63% (247/392) of cases, compared to LLaMA2's 38% (149/392, P<.001). Furthermore, the top diagnosis was accurately identified by LLaMA3 in 33.9% (133/392) of cases, significantly higher than the 22.7% (89/392) achieved by LLaMA2 (P<.001). The analysis across various medical specialties revealed variations in diagnostic performance with LLaMA3 consistently outperforming LLaMA2. The results reveal that the LLaMA3 model significantly outperforms LLaMA2 per diagnostic performance, with a higher percentage of case reports having the final diagnosis listed within the top 10, top 5, and as the top diagnosis. Overall diagnostic performance improved almost 1.5 times from LLaMA2 to LLaMA3. These findings support the rapid development and continuous refinement of generative AI systems to enhance diagnostic processes in medicine. However, these findings should be carefully interpreted for clinical application, as generative AI, including the LLaMA series, has not been approved for medical applications such as AI-enhanced diagnostics.

Unprecedented presentation of pelvic synovial sarcoma: A compelling case report

Synovial sarcoma (SS) is a rare tumor that typically arises in various body regions, with its manifestation in the abdominal-pelvic area being exceptionally uncommon. However, despite its rarity in this location, it often metastasizes to the thorax. Despite its slow growth, it can be misdiagnosed as benign in less than 10% of cases. Diagnosis necessitates identification of the SYT-SSX fusion transcript, alongside immunohistochemical and cytogenetic assessments. Treatment involves surgical resection followed by radiotherapy for localized tumors, while chemotherapy is necessary for metastatic cases. Despite treatment, synovial sarcomas have a high recurrence rate, with about half recurring within two years. We report the case of a 73-year-old patient from sub-Saharan Africa who presented to the emergency room with obstructive symptoms, revealing a mass near the anus compressing the rectum upon imaging, causing an obstructive syndrome. Despite initial tumor resection, histological analysis identified a neurofibromatous origin. Twenty months later, recurrence prompted a hospital visit, revealing thoracic metastases necessitating surgery and chemotherapy. Subsequent analysis confirmed perianal synovial sarcoma. This case underscores the rarity of pelvic-anal synovial sarcoma and emphasizes the importance of early detection and proper management. Synovial sarcoma should figure prominently on the list of differential diagnoses of high-grade pelvic masses due to the importance of adjuvant chemotherapy.

An exploratory study on the potential of ChatGPT as an AI-assisted diagnostic tool for visceral leishmaniasis

Visceral leishmaniasis (VL) is a severe parasitic disease that poses significant diagnostic challenges due to its complex presentation and the necessity for comprehensive diagnostic methods. This exploratory study investigates the potential of Chat Generative Pre-trained Transformer (ChatGPT)/GPT-4, an artificial intelligence (AI) chatbot, in assisting the diagnostic process for VL. We evaluated the diagnostic accuracy of ChatGPT/GPT-4 in generating differential diagnosis lists for eight clinical vignette cases of VL, authored by a Brazilian infectious disease doctor. Our findings reveal that ChatGPT/GPT-4 included VL in the top five differential diagnoses in 75% of the cases (95% confidence interval [CI]: 40.1 &amp;ndash; 93.7%) and identified VL as the top diagnosis in 50% of the cases (95% CI: 30.3 &amp;ndash; 86.5%). These results underscore the high potential of ChatGPT/GPT-4 as an AI-assisted diagnostic tool, which is capable of providing accurate differential diagnoses and assisting healthcare professionals in resource-limited settings. The study highlights the broader applicability of AI chatbots in medical diagnostics, not only for common conditions but also for specialized and less prevalent diseases like VL. By integrating AI tools into the diagnostic workflow, healthcare providers can enhance their diagnostic accuracy and efficiency, ultimately improving patient outcomes. This research contributes to the growing body of evidence supporting the utility of AI in healthcare and underscores the need for further studies to validate these findings across larger and more diverse clinical scenarios.

The Onerous task of managing paroxysmal nocturnal hemoglobinuria in a Low resource setting: a case report. A hematologist’s experience

Introduction: Paroxysmal nocturnal hemoglobinuria (PNH) is a form of red cell membrane defect characterized by increased sensitivity to complement-mediated cell lysis, resulting in intravascular hemolytic anemia, passage of hemoglobin-containing urine, a high risk of venous thrombosis and progression to pancytopenia.The diagnosis of PNH is based on the flow cytometric (FCM) detection of peripheral blood cell clones. Such clones lack expression of the surface molecules linked to the glycosylphosphatidylinositol (GPI) anchors. The underlying defect is a somatic mutation of the short arm of the phosphatidylinositol glycan class A gene (PIG-A). Case report: We report a case of a 34-year-old male, with recurrent hemolytic anemia and dural venous sinus thrombosis found to have PNH by flow cytometry. He is currently on anticoagulation, iron and folate supplements, intermittent steroids for hemolytic episodes as we await complement inhibitors. Conclusion: As part of the diagnostic workup for patients presenting with recurrent hemolytic anemia and thrombosis in unusual sites, clinicians should include PNH on the list of differential diagnoses. Effort should be taken to characterize the red urine reported on the urine dipstick as blood/hemoglobin by microscopy to differentiate hematuria and hemoglobinuria and order for flow cytometry as this has implications on patient management. Keywords: Thrombosis; hemolysis; complement; paroxysmal nocturia; hemoglobinuria.

Comparative Study to Evaluate the Accuracy of Differential Diagnosis Lists Generated by Gemini Advanced, Gemini, and Bard for a Case Report Series Analysis: Cross-Sectional Study.

Generative artificial intelligence (GAI) systems by Google have recently been updated from Bard to Gemini and Gemini Advanced as of December 2023. Gemini is a basic, free-to-use model after a user's login, while Gemini Advanced operates on a more advanced model requiring a fee-based subscription. These systems have the potential to enhance medical diagnostics. However, the impact of these updates on comprehensive diagnostic accuracy remains unknown. This study aimed to compare the accuracy of the differential diagnosis lists generated by Gemini Advanced, Gemini, and Bard across comprehensive medical fields using case report series. We identified a case report series with relevant final diagnoses published in the American Journal Case Reports from January 2022 to March 2023. After excluding nondiagnostic cases and patients aged 10 years and younger, we included the remaining case reports. After refining the case parts as case descriptions, we input the same case descriptions into Gemini Advanced, Gemini, and Bard to generate the top 10 differential diagnosis lists. In total, 2 expert physicians independently evaluated whether the final diagnosis was included in the lists and its ranking. Any discrepancies were resolved by another expert physician. Bonferroni correction was applied to adjust the P values for the number of comparisons among 3 GAI systems, setting the corrected significance level at P value <.02. In total, 392 case reports were included. The inclusion rates of the final diagnosis within the top 10 differential diagnosis lists were 73% (286/392) for Gemini Advanced, 76.5% (300/392) for Gemini, and 68.6% (269/392) for Bard. The top diagnoses matched the final diagnoses in 31.6% (124/392) for Gemini Advanced, 42.6% (167/392) for Gemini, and 31.4% (123/392) for Bard. Gemini demonstrated higher diagnostic accuracy than Bard both within the top 10 differential diagnosis lists (P=.02) and as the top diagnosis (P=.001). In addition, Gemini Advanced achieved significantly lower accuracy than Gemini in identifying the most probable diagnosis (P=.002). The results of this study suggest that Gemini outperformed Bard in diagnostic accuracy following the model update. However, Gemini Advanced requires further refinement to optimize its performance for future artificial intelligence-enhanced diagnostics. These findings should be interpreted cautiously and considered primarily for research purposes, as these GAI systems have not been adjusted for medical diagnostics nor approved for clinical use.

The Role of Digital Dermoscopy and Follow-Up in the Detection of Amelanotic/Hypomelanotic Melanoma in a Group of High-Risk Patients-Is It Useful?

The prognosis, outcome, and overall survival of melanoma patients improve with early diagnosis which has been facilitated in the past few decades with the introduction of dermoscopy. Further advancements in dermoscopic research, coupled with skilled, educated dermatologists in dermoscopy, have contributed to timely diagnoses. However, detecting amelanotic and hypomelanotic melanoma remains a challenge even to the most skilled experts because these melanomas can mimic inflammatory diseases, numerous benign lesions, and non-melanoma skin cancers. The list of the possible differential diagnoses can be long. Melanoma prediction without the pigment relies only on vascular criteria, and all classic dermoscopic algorithms have failed to fulfill our expectations. In fact, the diagnosis of amelanotic and hypomelanotic melanomas is very challenging, which is why every tool in detecting these lesions is of significance. This review aims to explore the current knowledge and the literature on the possibility of detecting amelanotic/hypomelanotic melanomas using sequential monitoring with digital dermoscopy and total body skin photography.

Screening for Fabry disease in patients with left ventricular hypertrophy in China: A multicentre and prospective study.

Left ventricular hypertrophy (LVH) is frequently detected via echocardiography in individuals with Fabry disease (FD), sometimes leading to confusion with hypertrophic cardiomyopathy (HCM) of other aetiologies. Considering this diagnosis challenge, FD should be included in the list of differential diagnosis for patients presenting with LVH. To address this concern, we conducted a prospective screening study in China, using dried blood spot (DBS) testing, to evaluate patients with unexplained LVH. Our study was designed as a nationwide, multicentre prospective investigation. A total of 1015 patients from 55 different centres who were diagnosed with LVH by echocardiography were screened in the study from September 2022 to December 2023. Demographic information, biochemistry data, echocardiography parameters and clinical observations were meticulously collected from all participants. The DBS method was used to assess α-galactosidase A (α-Gal A) activity in males and both α-Gal A and globotriaosylsphingosine (lyso-Gb3) levels in females. The final screening population included 906 patients (589 males, 65%) with LVH, characterized by a mean maximal myocardial thickness of 14.8±4.6mm and an average age of 56.9±17.2years. In total, 43 patients (38 males, 5 females) exhibited low α-Gal A activity measurement (<2.2μmol/L), while 21 patients (10 males, 11 females) presented low α-Gal A activity or elevated lyso-Gb3 levels (>1.1ng/mL). Among these patients, eight individuals (7 males and 1 female) were genetically confirmed to harbour pathogenic GLA mutations, resulting in a total prevalence of 0.88%. Compared with patients without FD, patients with FD tended to have proteinuria (75% vs. 21.2%, P=0.001), family history of HCM (37.5% vs. 2.3%, P<0.01) and neuropathic pain (37.5% vs. 4.4%, P<0.01) but lower systolic blood pressure (118.5±12.5 vs. 143.3±29.3mmHg, P=0.017). Five mutations were previously recognized as associated with FD while the remaining two, p.Asp313Val (c.938A>T) and c.547+3A>G, were deemed potentially pathogenic. Subsequent familial validation post-diagnosis identified an additional 14 confirmed cases. This pioneering screening study for FD among Chinese patients with unexplained LVH using DBS measurement, revealed an FD detection rate of 0.88%. Our findings confirmed that the combined measurement of lyso-Gb3 and α-Gal A activity is beneficial for primary screening of FD in patients with LVH. Given the availability of efficacious therapies and the value of cascade screening in extended families, early detection of FD in LVH patients is clinically important.

Popliteal Artery Entrapment Syndrome: Updates for Evaluation, Diagnosis, and Treatment.

Popliteal artery entrapment syndrome remains difficult to diagnose. Meanwhile, our limited knowledge and understanding make treatment decisions complex. The list of differential diagnoses for exertional leg pain is broad. Oftentimes, patients exhibit confounding and coexisting diagnoses. However, accurate and rapid diagnosis of popliteal artery entrapment syndrome is essential to reduce potential lasting damage to the popliteal artery. A combination of clinical history, physical examination, ankle-brachial index, along with dynamic and static imaging such as duplex ultrasound, computed tomography angiogram, and magnetic resonance angiography, aids diagnosis. Surgical treatment may be definitive depending on the type of popliteal artery entrapment syndrome, but there have been recent advances in diagnostics with intravascular ultrasound and nonsurgical treatment with botulinum toxin type A. Further research is needed to standardize diagnostic criteria, uncover innovative diagnostic methods, and validate promising nonoperative treatment options.

Cardiac biomarker profiles in dogs with naturally occurring precapillary pulmonary hypertension

Introduction/ObjectivesThis study evaluated circulating amino-terminal pro-B-type natriuretic peptide (NT-proBNP), amino-terminal pro-A-type natriuretic peptide (NT-proANP), and cardiac troponin I (cTnI) concentrations in dogs with precapillary pulmonary hypertension (Pre-PH) and control dogs with respiratory clinical signs but no Pre-PH. AnimalsTwenty-six dogs (17 affected, and 9 controls) were involved in the study. Materials and MethodsThis was a sub-study of a large prospective single-center observational study. Dogs underwent blood sample collection, physical examination, and echocardiographic evaluation. Pre-PH was diagnosed when a calculated right ventricular-to-right atrial pressure gradient (RV:RA PG) measuring ≥40 mmHg was identified echocardiographically, barring right ventricular outflow obstruction and/or left-sided cardiac disease. ResultsTwo, nine, and six dogs had mild, moderate, and severe Pre-PH, respectively. Plasma concentrations of NT-proBNP, NT-proANP, and cTnI were significantly higher in the affected group than in the control group (P=0.020, P=0.009, P=0.011, respectively). There was a positive correlation between RV:RA PG and NT-proBNP (r = 0.52), NT-proANP (r = 0.54), and cTnI (r = 0.67) concentrations. DiscussionPre-PH should be included in the differential diagnosis list of elevated cardiac biomarker concentrations in dogs with respiratory signs. Study LimitationsStrict selection criteria reduced group sizes. There were rare missing data points. The diagnosis of Pre-PH was obtained from Doppler echocardiographic RV:RA PG. The disease process causing Pre-PH was not evaluated histopathologically. ConclusionsCirculating cardiac biomarker concentrations are increased in dogs with Pre-PH and there is a positive correlation between RV:RA PG and NT-proBNP, NT-proANP, and cTnI concentrations.

Clinical and Dermoscopic Patterns of Basal Cell Carcinoma and Its Mimickers in Skin of Color: A Practical Summary.

The diagnosis of basal cell carcinoma (BCC) in dark phototypes can be a challenging task due to the lack of relevant clues and its variable presentation. In this regard, there is growing evidence that dermoscopy may benefit the recognition of BCC even for skin of color (SoC). The objective of this review is to provide an up-to-date overview on clinical and dermoscopic patterns of BCC in SoC, also comparing such findings with those of the main clinical mimickers reported in the literature. A comprehensive search of the literature through the PubMed electronic database was carried out in order to identify papers describing the clinical and dermoscopic features of BCC in dark phototypes (IV-VI). By finding macroscopic clinical presentations of BCCs in SoC patients and any possible clinical mimickers considered in the retrieved papers, we built a differential diagnosis list and analyzed the dermoscopic findings of such conditions to facilitate the diagnosis of BCC. BCC in darker skin may present as pigmented nodular lesions, pigmented patches or plaques, ulcers, erythematous nodular lesions, erythematous plaques or patches, or scar-like lesions, depending on its subtype and body site. The differential diagnosis for BCC in patients with SoC includes squamous cell carcinoma, melanoma, nevi, adnexal tumors and sebaceous keratosis. Additionally, it differs from that of Caucasians, as it also includes lesions less common in fair skin, such as dermatosis papulosa nigra, melanotrichoblastoma, and pigmented dermatofibrosarcoma protuberans, and excludes conditions like actinic keratosis and keratoacanthoma, which rarely appear in darker skin. The resulting differences also include infectious diseases such as deep cutaneous mycosis and inflammatory dermatoses. The most prevalent differentiating dermoscopic feature for BCC includes blue, black and gray dots, though arborizing vessels still remain the predominant BCC feature, even in dark phototypes. Diagnostic approach to BCC in dark-skinned patients varies due to the prevalence of dermoscopy findings associated with hyperpigmented structures. Clinicians should be aware of such points of differentiation for a proper management of this tumor in SoC.

Evaluation of the Clinical and Paraclinical Symptoms of Children Intoxicated With Amphetamines in Akbar Hospital of Mashhad, Iran 2021-2022.

Amphetamine poisoning in children is a significant public health concern due to its potential for severe adverse effects on physical and mental health. In this article, we describe a case series of 29 children under the age of 14 presenting with sympathomimetic toxidrome due to confirmed amphetamine poisoning. In this cross-sectional study, children (1 month to 14 years old) who were hospitalized in the emergency and pediatric departments of Imam Reza and Akbar Hospital in Mashhad from the beginning of April 2021 to September 2022 were identified with the diagnosis of amphetamine poisoning. Their demographic, clinical, and paraclinical (laboratory and electrocardiogram) information, which was included in the relevant checklists, were analyzed and reported. Out of 29 children included in the study, 18 (62%) were male and the rest were female. The average age of children was 44.75 ±43.9 months; most of them were less than 4 years old. In all patients, glasgow coma scale (GCS) was 15. In 25.92% of cases (8 patients) blood pressure was above the 90% percentile for age and sex, 82.60% (24 cases) were tachycardia, 72.41% crying (21 cases), 86.20% were restless (25 cases), 10.34% tremor (3 cases), 10.34% had body pain (3 cases), and 6.89% (2 cases) had delusions. None of the subjects had a seizure. The serum level of creatine phosphokinase was on average 771.99 ± 966 units/L. All children had an increase in creatine phosphokinase (CPK) and 25.9% of children had an increase in CPK to more than 1000 units/L. Based on the results, in children with symptoms such as restlessness, crying, and tachycardia, poisoning with amphetamine group substances should be included in the differential diagnosis list, and also measuring CPK and investigating the possible occurrence of rhabdomyolysis in cases of poisoning of children with amphetamine seems necessary.

Accuracy Evaluation of GPT-Assisted Differential Diagnosis in Emergency Department.

In emergency department (ED) settings, rapid and precise diagnostic evaluations are critical to ensure better patient outcomes and efficient healthcare delivery. This study assesses the accuracy of differential diagnosis lists generated by the third-generation ChatGPT (ChatGPT-3.5) and the fourth-generation ChatGPT (ChatGPT-4) based on electronic health record notes recorded within the first 24 h of ED admission. These models process unstructured text to formulate a ranked list of potential diagnoses. The accuracy of these models was benchmarked against actual discharge diagnoses to evaluate their utility as diagnostic aids. Results indicated that both GPT-3.5 and GPT-4 reasonably accurately predicted diagnoses at the body system level, with GPT-4 slightly outperforming its predecessor. However, their performance at the more granular category level was inconsistent, often showing decreased precision. Notably, GPT-4 demonstrated improved accuracy in several critical categories that underscores its advanced capabilities in managing complex clinical scenarios.

Role of digital subtraction angiography in acute cervical spinal cord ischemia.

Ascertaining the etiology of cervical spinal cord dysfunction presents a challenge to clinicians, as the list of differential diagnoses is extensive. Although compressive and inflammatory disorders are common and should be considered immediately, vascular causes are similarly important and acute. The overlap of clinical, magnetic resonance imaging, and cerebrospinal fluid features among the causes of myelopathies may lead to erroneous diagnoses. Such errors may be compounded if routine vascular imaging does not reveal the underlying vasculopathy. We present here three cases in which computed tomography angiography and magnetic resonance angiogram could not clarify the nature of an acute myelopathy, whereas digital subtraction angiography established the diagnosis of spinal cord ischemia.

Survey of Gastrointestinal Parasites and Lungworms in Cats and Dogs from Terceira and São Miguel Islands, Azores.

Parasitic diseases can affect animal health and welfare, and they may also constitute a danger to public health, particularly in island ecosystems. Fecal samples were collected from 205 dogs and 115 cats on the islands of São Miguel and Terceira, Azores archipelago (Portugal), using the Willis flotation technique and modified Baermann method, for further analysis. The overall prevalence of gastrointestinal parasitism in dogs was 53%, with the following results: Ancylostomatidae (hookworms) (42.44%), Trichuris vulpis (17.56%), Toxocara canis (12.68%) and Cystoisospora spp. (4.39%). In cats, the overall prevalence was also 53%, with the following results: Toxocara cati (31.3%), Ancylostomatidae (30.43%), Cystoisospora spp. (14.78%) and Trichuris sp. (0.87%). The prevalence of lungworms was 0.49% in canines and 20.87% in felines, with Angiostrongylus vasorum and Aelurostrongylus abstrusus species being detected in dogs and cats, respectively. The present survey detected a high prevalence of gastrointestinal infection, in both dogs and cats, probably because the samples came mainly from kennels and catteries and due to the peculiar climatic conditions in this insular territory, with mild temperature and high relative humidity. A considerable prevalence of aelurostrongylosis was also detected (20.87%), so it should be included in the list of differential diagnoses of diseases concerning the respiratory tract in cats of the archipelago.

Evaluation of diseases complicating long COVID: A retrospective chart review.

Evidence for the pathogenesis and treatment of postacute coronavirus disease 2019 (COVID-19) (long COVID) is lacking. As long COVID symptoms are predicted to have an impact on the global economy, clarification of the pathogenesis is urgently needed. Our experiences indicated that some symptoms were complicated by diseases established before the COVID-19 pandemic. Using a retrospective, cross-sectional study, we aimed to evaluate the diseases complicating long COVID. Using the medical records of patients with confirmed COVID-19 exhibiting residual symptoms lasting ≥60 days postinfection who visited our clinic in January 2021-February 2023, we investigated the symptoms and diseases observed. We identified diseases that occurred after COVID-19 and excluded those that were exacerbations of existing diseases. During the first visit, the most common symptoms reported in a total of 798 patients were fatigue (523 patients), anxiety (349 patients), and lack of motivation (344 patients). Complicating diseases were observed in 452 patients (57%). There were 115, 65, and 60 patients with postural tachycardia syndrome, postural syndrome without tachycardia, and mood disorders, respectively. Some diseases requiring immediate treatment included pulmonary thromboembolism, purulent shoulder arthritis, cerebellopontine angle tumors, myasthenia gravis, and cervical myelopathy. Not all symptoms that occur after COVID-19 should be treated as long COVID. Similar to normal medical treatment, a list of differential diagnoses should be maintained based on symptoms to obtain definitive diagnoses.

Evaluation of large language models as a diagnostic aid for complex medical cases.

The use of large language models (LLM) has recently gained popularity in diverse areas, including answering questions posted by patients as well as medical professionals. To evaluate the performance and limitations of LLMs in providing the correct diagnosis for a complex clinical case. Seventy-five consecutive clinical cases were selected from the Massachusetts General Hospital Case Records, and differential diagnoses were generated by OpenAI's GPT3.5 and 4 models. The mean number of diagnoses provided by the Massachusetts General Hospital case discussants was 16.77, by GPT3.5 30 and by GPT4 15.45 (p < 0.0001). GPT4 was more frequently able to list the correct diagnosis as first (22% versus 20% with GPT3.5, p = 0.86), provide the correct diagnosis among the top three generated diagnoses (42% versus 24%, p = 0.075). GPT4 was better at providing the correct diagnosis, when the different diagnoses were classified into groups according to the medical specialty and include the correct diagnosis at any point in the differential list (68% versus 48%, p = 0.0063). GPT4 provided a differential list that was more similar to the list provided by the case discussants than GPT3.5 (Jaccard Similarity Index 0.22 versus 0.12, p = 0.001). Inclusion of the correct diagnosis in the generated differential was correlated with PubMed articles matching the diagnosis (OR 1.40, 95% CI 1.25-1.56 for GPT3.5, OR 1.25, 95% CI 1.13-1.40 for GPT4), but not with disease incidence. The GPT4 model was able to generate a differential diagnosis list with the correct diagnosis in approximately two thirds of cases, but the most likely diagnosis was often incorrect for both models. In its current state, this tool can at most be used as an aid to expand on potential diagnostic considerations for a case, and future LLMs should be trained which account for the discrepancy between disease incidence and availability in the literature.