Matching Rate Research Articles

Higher Grit Scores Are Associated With Greater Scholarly Productivity Among Orthopaedic Surgery Residents

Introduction: Scholarly research is a core component of resident education, designed to advance the orthopaedic field while facilitating an evidence-based approach to patient care. Grit, defined as trait-level perseverance, has been shown in prior studies to predict clinical performance, burnout rates, and residency match rates. No previous study has investigated the relationship between grit and research productivity. Study Description: This study is a cross-sectional survey of orthopaedic surgery residents from the American Academy of Orthopaedic Surgeons. Residents were assessed via online survey of scholarly productivity and the 12-item grit scale. Methods: Surveys distributed to the Academy of Orthopaedic Surgeons residents assessed the number of peer-reviewed publications, presentations, first author publications, book chapters, and articles. Respondents were also evaluated through the 12-item grit scale, with scores ranging from 1 (minimum grit) to 5 (maximum grit). Residents were divided into low (score ≤3.4), medium (score: 3.5 to 3.9), and high grit (score ≥4.0) cohorts. Analysis of variance analysis compared the cohorts regarding the scholarly productivity metrics. Linear regression models analyzed grit score as a continuous variable. Findings were statistically significant at P < 0.05. Results: The mean grit score was 3.8. The high grit cohort was more likely to have increased publications, presentations, articles, and first authorship (P < 0.0001). Analyzed as a continuous variable, increased grit positively correlated with increased publications, presentations, articles, first authorship, and book chapters (P < 0.0001). Conclusions: The results suggest that higher grit scores are associated with scholarly productivity.

Testing the Capability of Embedding-Based Alignments on the GST Superfamily Classification: The Role of Protein Length.

In order to shed light on the usage of protein language model-based alignment procedures, we attempted the classification of Glutathione S-transferases (GST; EC 2.5.1.18) and compared our results with the ARBA/UNI rule-based annotation in UniProt. GST is a protein superfamily involved in cellular detoxification from harmful xenobiotics and endobiotics, widely distributed in prokaryotes and eukaryotes. What is particularly interesting is that the superfamily is characterized by different classes, comprising proteins from different taxa that can act in different cell locations (cytosolic, mitochondrial and microsomal compartments) with different folds and different levels of sequence identity with remote homologs. For this reason, GST functional annotation in a specific class is problematic: unless a structure is released, the protein can be classified only on the basis of sequence similarity, which excludes the annotation of remote homologs. Here, we adopt an embedding-based alignment to classify 15,061 GST proteins automatically annotated by the UniProt-ARBA/UNI rules. Embedding is based on the Meta ESM2-15b protein language. The embedding-based alignment reaches more than a 99% rate of perfect matching with the UniProt automatic procedure. Data analysis indicates that 46% of the UniProt automatically classified proteins do not conserve the typical length of canonical GSTs, whose structure is known. Therefore, 46% of the classified proteins do not conserve the template/s structure required for their family classification. Our approach finds that 41% of 64,207 GST UniProt proteins not yet assigned to any class can be classified consistently with the structural template length.

Redesigned Entrustable Professional Activity (EPA) Assessments Reduce Grade Inflation in the Experiential Setting

ObjectiveThis study aims to evaluate the impact of redesigning an entrustable professional activities (EPAs) assessment tool on the accuracy of student performance assessment within pharmacy education. MethodsThe study used retrospective programmatic data for students on clinical rotations over a 3-year period and compared entrustment levels assigned by preceptors with suggested entrustment levels. This tool was redesigned to separate formative EPA feedback from final grade determination. Data were analyzed using chi-squared tests to identify trends in students ABOVE, AT, or BELOW the suggested entrustment levels. Additionally, to account for intercohort variability, the relationship between students ABOVE the suggested level of entrustment and postgraduate metrics was examined. ResultsAfter the implementation of the revised tool, there was a significant decrease (−3%) in the percentage of students scoring ABOVE the suggested entrustment levels and an increase in the percentage of students scoring AT (+1%) or BELOW (+2%) the suggested entrustment levels. Changes were also observed in individual patient care settings, with a decrease in grade inflation and an increase in accurate assessments. North American Pharmacist Licensure Examination (NAPLEX) pass rates, residency match rates, and grade point average did not correlate with entrustment levels. ConclusionThe redesigned EPA assessment tool demonstrated a decrease in grade inflation resulting in more accurate assessments. The tool’s focus on holistic grading and narrative descriptors contributed to better alignment between preceptor assessment and school-suggested achievement levels. This study suggests that EPA assessments in pharmacy education could benefit from a stronger emphasis on formative feedback and the use of holistic assessment methods for final grade determinations. The findings underscore the potential advantages of considering a separation between EPA scoring and final grades, prompting the Academy to explore their assessment practices to better reflect student performance in clinical experiences.

Impact of sarcopenia on the prognosis of patients with advanced non-small cell lung cancer treated with antiangiogenic therapy: A propensity score matching analysis.

Limited information is available regarding the impact of sarcopenia on the prognosis of antiangiogenic therapy in individuals with advanced non-small cell lung cancer (NSCLC). This study primarily sought to examine the prognostic significance of sarcopenia in individuals with advanced NSCLC undergoing antiangiogenic therapy. We retrospectively enrolled all patients who met the inclusion and exclusion criteria from 2019 to 2021 at Nantong University Hospital. Patients were grouped according to the presence or absence of sarcopenia. After propensity score matching (PSM), progression-free survival (PFS), overall survival (OS), and adverse event rates were compared between the two groups. Factors associated with prognosis were screened using univariate and multivariate analyses. A total of 267 patients were included, with a total of 201 matched at baseline after PSM (77 in the sarcopenia group and 124 in the non-sarcopenia group). The sarcopenia group had lower PFS (p = 0.043) and OS (p = 0.011) than the non-sarcopenia group and a higher incidence of adverse events (p = 0.044). Multivariate analysis suggested that sarcopenia is an independent prognostic risk factor for OS in advanced NSCLC patients receiving antiangiogenic therapies (p = 0.009). Results of subgroup analyses showed some differences in the impact of sarcopenia on survival prognosis in populations with different characteristics. Patients with advanced NSCLC with comorbid sarcopenia exhibit a worse prognosis when treated with antiangiogenic therapy, and preventing and ameliorating sarcopenia may lead to better survival outcomes in patients with advanced NSCLC.

An aggregate matching and pick-up model for mobility-on-demand services

This paper presents an Aggregate Matching and Pick-up (AMP) model to delineate the matching and pick-up processes in mobility-on-demand (MoD) service markets by explicitly considering the matching mechanisms in terms of matching intervals and matching radii. With passenger demand rate, vehicle fleet size and matching strategies as inputs, the AMP model can well approximate drivers’ idle time and passengers’ waiting time for matching and pick-up by considering batch matching in a stationary state. Properties of the AMP model are then analyzed, including the relationship between passengers’ waiting time and drivers’ idle time, and their changes with market thickness, which is measured in terms of the passenger arrival rate (demand rate) and the number of active vehicles in service (supply). The model can also unify several prevailing inductive and deductive matching models used in the literature and spell out their specific application scopes. In particular, when the matching radius is sufficiently small, the model reduces to a Cobb–Douglas type matching model proposed by Yang and Yang (2011) for street-hailing taxi markets, in which the matching rate depends on the pool sizes of waiting passengers and idle vehicles. With a zero matching interval and a large matching radius, the model reduces to Castillo model developed by Castillo et al. (2017) that is based on an instant matching mechanism, or a bottleneck type queuing model in which passengers’ matching time is derived from a deterministic queue at a bottleneck with the arrival rate of idle vehicles as its capacity and waiting passengers as its customers. When both the matching interval and matching radius are relatively large, the model also reduces to the bottleneck type queuing model. The performance of the proposed AMP model is verified with simulation experiments.

Crystal Structure Determination from Powder Diffraction Patterns with Generative Machine Learning.

Powder X-ray diffraction (PXRD) is a cornerstone technique in materials characterization. However, complete structure determination from PXRD patterns alone remains time-consuming and is often intractable, especially for novel materials. Current machine learning (ML) approaches to PXRD analysis predict only a subset of the total information that comprises a crystal structure. We developed a pioneering generative ML model designed to solve crystal structures from real-world experimental PXRD data. In addition to strong performance on simulated diffraction patterns, we demonstrate full structure solutions over a large set of experimental diffraction patterns. Benchmarking our model, we predicted the structure for 134 experimental patterns from the RRUFF database and thousands of simulated patterns from the Materials Project on which our model achieves state-of-the-art 42 and 67% match rate, respectively. Further, we applied our model to determine the unreported structures of materials such as NaCu2P2, Ca2MnTeO6, ZrGe6Ni6, LuOF, and HoNdV2O8 from the Powder Diffraction File database. We extended this methodology to new materials created in our lab at high pressure with previously unsolved structures and found the new binary compounds Rh3Bi, RuBi2, and KBi3. We expect that our model will open avenues toward materials discovery under conditions which preclude single crystal growth and toward automated materials discovery pipelines, opening the door to new domains of chemistry.

ChatGPT-based meta-analysis for evaluating the temporal and spatial characteristics of deoxynivalenol contamination in Chinese wheat

Deoxynivalenol (DON) is a major source of mycotoxins in wheat. However, there is a lack of systematic reporting of the overall contamination status in China, hindering comprehensive assessments. In this study, we utilized a meta-analysis approach based on ChatGPT to systematically analyze DON contamination in wheat-growing regions in China, as reported in the literature from 2010 to 2021. By optimizing the query processes and refining the methodology keywords using ChatGPT, efficient screening, data identification, and literature extraction were achieved for the first time during the meta-analysis data acquisition phase. The matching rates for the screening and extraction of 1091 articles were 100 % and 95.4 %, respectively, resulting in a 20.5-fold work efficiency increase compared to that by manual operations. Meta-subgroup analysis by province and year revealed significant spatiotemporal heterogeneity in DON contamination in the wheat-growing regions of China. Furthermore, the relationship between climate factors and DON levels in wheat was investigated to illustrate the spatial and temporal heterogeneity of DON in Chinese wheat. The results showed that DON concentrations were mainly influenced by relative humidity and precipitation during the wheat-growing season. This novel ChatGPT-assisted meta-analysis approach provides valuable insights and offers a promising method for efficient meta-analyses in other fields.

A 12-Year Analysis of Demand and Supply for Hand Surgery Training in the United States.

The consistent recruitment of quality applicants is critical to the integrity of the US surgical workforce. This study assesses the demand and supply for hand surgery training in the US. This was a cross-sectional analysis of US hand surgery fellowship applicants from 2012 to 2023. Data were obtained from the National Resident Matching Program. Primary outcomes were annual number of hand surgery fellowship applicants, training positions, training programs, and unfilled training positions. Temporal trends were assessed with linear regression. The annual number of hand surgery programs (73 to 93, 27.4% increase, P<0.001) and training positions (150 to 193, 28.7% increase, P<0.001) increased over the study period while the annual number of applicants remained unchanged (199 to 198, P=0.431). The applicant-to-training position ratio decreased over the study period (1.3 to 1.0, P=0.001). The overall match rate increased (73.4% to 95.5%, P<0.001) along with the mean number of submitted ranks per applicant (8.2 to 14.9, P<0.001). The distribution of US allopathic graduates, (91.9%), US osteopathic graduates (4.6%) and international medical graduates (3.5%) was similar over the study period. The rate of applicants matching at their first-choice fellowship increased over the study period (28.1% to 38.4%, P=0.003) while the rate of unmatched applicants decreased (26.6% to 4.5%, P<0.001). There was consistent growth in the number of hand surgery programs and training positions without a commensurate increase in the number of interested applicants. Increased recruitment efforts are necessary to ensure adequate demand for available training positions in hand surgery.

Targeted therapy guided by circulating tumor DNA analysis in advanced gastrointestinal tumors.

Although comprehensive genomic profiling has become standard in oncology for advanced solid tumors, the full potential of circulating tumor DNA (ctDNA)-based profiling in capturing tumor heterogeneity and guiding therapy selection remains underexploited, marked by a scarcity of evidence on its clinical impact and the assessment of intratumoral heterogeneity. The GOZILA study, a nationwide, prospective observational ctDNA profiling study, previously demonstrated higher clinical trial enrollment rates using liquid biopsy compared with tissue screening. This updated analysis of 4,037 patients further delineates the clinical utility of ctDNA profiling in advanced solid tumors, showcasing a significant enhancement in patient outcomes with a 24% match rate for targeted therapy. Patients treated with matched targeted therapy based on ctDNA profiling demonstrated significantly improved overall survival compared with those receiving unmatched therapy (hazard ratio, 0.54). Notably, biomarker clonality and adjusted plasma copy number were identified as predictors of therapeutic efficacy, reinforcing the value of ctDNA in reflecting tumor heterogeneity for precise treatment decisions. These new insights into the relationship between ctDNA characteristics and treatment outcomes advance our understanding beyond the initial enrollment benefits. Our findings advocate for the broader adoption of ctDNA-guided treatment, signifying an advancement in precision oncology and improving survival outcomes in advanced solid tumors.

A Hybrid Approach for Image Acquisition Methods Based on Feature-Based Image Registration.

This paper presents a novel hybrid approach to feature detection designed specifically for enhancing Feature-Based Image Registration (FBIR). Through an extensive evaluation involving state-of-the-art feature detectors such as BRISK, FAST, ORB, Harris, MinEigen, and MSER, the proposed hybrid detector demonstrates superior performance in terms of keypoint detection accuracy and computational efficiency. Three image acquisition methods (i.e., rotation, scene-to-model, and scaling transformations) are considered in the comparison. Applied across a diverse set of remote-sensing images, the proposed hybrid approach has shown marked improvements in match points and match rates, proving its effectiveness in handling varied and complex imaging conditions typical in satellite and aerial imagery. The experimental results have consistently indicated that the hybrid detector outperforms conventional methods, establishing it as a valuable tool for advanced image registration tasks.

Approximating neural distinguishers using differential-linear imbalance

At CRYPTO 2019, Gohr first proposed neural distinguishers (NDs) on SPECK32, which are superior to the distinguishers based on the differential distribution table (DDT). Benamira et al. noted that NDs rely on the differential distribution of the last three rounds, and Bao et al. pointed out that NDs depend on the strong correlations between the bit values of ciphertext pairs satisfying the expected differential. Hence, one may guess that there exist deep relations between NDs and the differential-linear imbalances. To approximate NDs under a single ciphertext pair, we utilize differential-linear imbalances to construct simplified distinguishers. These newly constructed distinguishers offer comparable distinguishing advantages to that of NDs but with reduced time complexities. For instance, one such simplified distinguisher has only 2-1.35\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2^{-1.35}$$\\end{document} of the original time complexity of NDs. Our experiments demonstrate that these new distinguishers achieve a matching rate of 98.2% for 5-round SPECK32 under a single ciphertext pair. Furthermore, we achieve the highest accuracies for 7-round and 8-round SPECK32 up to date by using a maximum of 512 ciphertext pairs. Finally, by replacing NDs with simplified distinguishers, we significantly reduce the time complexities of differential-neural attacks on 11–14 rounds of SPECK32.

Linking Education Data in Wales (UK) – Methodology and Lessons Learned

There are separate data collections across education phases in Wales (UK). Each use a different unique learner identifier. The matched education dataset project used advanced data linking methods to produce a set of pseudo identifiers for each learner that can be matched back to the original datasets to undertake specific, de-identified analysis. The first phase of the project involved data cleaning, preparation, and the creation of new derived variables. The second phase established the linking methodology, developing and advanced data linking techniques and algorithms including frequency matching and phonetic string comparators. At each stage of the project the data linking was sequential, ranging from exact to more fuzzy matching. During the code development the approach was uniquely tailored to each data set and constantly fine-tuned to ensure the highest possible match rate while reducing potential for false matches. The resulting linked education data sets are used in a number of ways for statistical and research purposes to support the formation of evidence-based policies. This has included research into raising the compulsory education age and the evaluation of learner journeys during the pandemic. Robust linked data facilitates analysis examining the progression of learners through the education system in Wales. There is a broad scope of future analysis planned and the matched education outputs will be used extensively in the evaluation of learner journeys post pandemic to inform Welsh Government policy. The matched education dataset project involved researching and upskilling in data linkage methodology, we hope to disseminate the lessons learned widely.

Linkage and Demographic Indexing for Nursing Dynamics in the Integrated Data Service

Objective and ApproachThis project links UK Nursing and Midwifery Council (NMC) Registrant data to the 2021 England and Wales Census (“Census 21”). The Office for National Statistics (ONS) has composed a framework of index tables to facilitate linkage between prominent datasets in the UK. In linking person-level data to Census 21, ONS’ Demographic Index enables onward linkage to other administrative datasets that are indexed alike. NMC Register and Census 21 records have been initially linked using bespoke deterministic and probabilistic methods, with resulting matches being joined to the Demographic Index. ONS’ Demographic Index Matching Service (DIMS) will also be used to link NMC data to the Demographic Index to enable comparisons between methods. ResultsAnalysis of both approaches will compare quality metrics such as match rate, recall and precision and tools will be used to assess bias in matches made through each method. These metrics will provide an insight into how the index matching services have been designed and whether improvements can be made to account for any shortcomings in their performance. ConclusionsHybrid approaches using both bespoke and indexing methods are proving valuable where sole use of the matching services is not suitable. This research will help ONS to refine which methods will yield the highest quality linkage and when. ImplicationsAs ONS pursues an ‘indexing first’ approach to linkage, the outcomes of this and similar projects will influence the future of utilising indexes in varied capacities when creating or maximising the potential of linked data.

Probabilistic Record Linkage for Families (PRLF): A Discussion of the Development and Validation of this Open-Source Linkage Tool

Linking administrative records across programs can yield person-centered information, including client characteristics, public service trajectories, and outcomes and help to answer policy-related questions. Several solutions are available for undertaking record linkage, producing linkage keys for merging data sources for positively matched pairs of records. In this session, we will demonstrate a new application of the Python RecordLinkage package to family-based record linkages with machine learning algorithms for probability scoring, which we call probabilistic record linkage for families (PRLF). First, we will demonstrate the utility of PRLF with a simulation of administrative records and assess linkage accuracy with variations in match rates and data degradation. Second, we will compare generalized linear model estimates across three record linkage solutions (PRLF, ChoiceMaker, and Link Plus). Findings from the simulation study indicate linkage accuracy is largely influenced by degradation (e.g., missing data fields, erroneous or incomplete values) compared to the proportion of simulated matches between datasets. Results from the methods comparison using real world data indicate that all three solutions, when optimized, provide similar results for researchers. We discuss the strengths of our process, such as the use of ensemble methods, to improve match accuracy. We then will identify caveats of record linkage in the context of administrative data. The tool was developed in Python to allow for researchers to work with open-source software and adjust the basic workflow to fit their linkage needs. We will identify several partnerships where this collaboration has worked successfully and empower attendees with access to this useful tool.

Leveraging Machine Learning to Combat Missingness and Error in Data

ObjectivePoor quality data confounds efforts to link clients across datasets. To combat this, we have trialed an approach which seeks to identify linkage candidates using associations with related services. We utilised a machine learning (ML) approach to query linkage candidates’ dataset associations and make predictions about whether candidates are likely to be a client of the service being integrated for linkage. ApproachUtilizing a K-nearest neighbors algorithm, we trained a model using 15 variables to predict whether a linkage candidate is a client of a family service dataset. We then evaluated the model's success, and modified its native performance to minimise false positive matches. Subsequently, we tested the validity of these predictions as linkage criteria by employing blocking strategies and linking the service dataset to our linkage spine. The trained model was then used to identify correct links against the spine. ResultsThe evaluation of the machine learning model yielded promising results, with high accuracy (88.5%) and precision (95.5%). Testing the predictions as linkage criteria resulted in highly accurate links ranging from 96.0% to 98.7% across different blocking strategies. Despite some records failing to establish any links to the spine, rates of false positive matches remained low (0.7% to 3.1%). ConclusionMissingness and inaccuracy in data remains a key problem for data linkage, and a robust approach is required to resolve complex linkage cases. However, these findings suggest that machine learning can present novel options for a toolbox of many approaches to link problem records to a linkage spine.

How ethnic name variations influence data linkage results: A population level study using public voter databases

ObjectivesIt has been shown that record linkage methods can induce bias in resulting datasets since the linkage quality may differ between ethno-racial groups. In this project, we used publicly available voter databases to investigate whether ethno-racial attributes can influence the similarities calculated using approximate string comparison functions on voters' names. ApproachWe used 11 annual snapshots of a publicly available US voter database between 2011 and 2021 with uniquely identifiable voter data. We extracted pairs of the same voter’s first and last name values from two consecutive snapshots where these values differ. We calculated string similarities and created similarity histograms for each of the available ethno-racial categories, and other sociodemographics variables such as gender and age. We characterized common patterns of name disagreements by ethno-racial groups and compared the shapes of these histograms using cumulative density distributions. Preliminary ResultsAcross the 10 snapshot pairs, eligible voters with non-missing first and last names are included (N2011/2012 = 6,193,001, N2020/2021 = 7,852,763), describing 124,009 first name changes and 485,807 last name changes. Results will be presented as a series of density plots. Using Jaro-Winkler similarity scores of 0.7, 0.8 and 0.9 as threshold, we examined whether match rates differ by ethno-racial categories. Conclusions and ImplicationsUsing the same string similarity function on names of individuals of different ethno-racial groups may lead to different distributions of the resulting similarity values. Understanding the patterns of ethno-racial-based name changes in your particular dataset is crucial on selecting linkage parameters that would minimise ethno-racial bias.

Kids’ Environment and Health Cohort

This presentation discusses Kids’ Environment and Health Cohort (KEHC), a bespoke Administrative Data Research UK (ADR UK) funded linkage project. KEHC will be a comprehensive and yearly updated dataset for children born in England from 2006 onwards, integrating health, education, pregnancy, and environmental data. This integrates data from different administrative systems including the Office for National Statistics, the National Health Service, and the Department for Education as well as open environmental data. This presentation will outline the deterministic and probabilistic linkage methods utilised and the quality assurance undertaken through clerical review and bias analysis. Additionally, we will delve into the challenges encountered in the process of data ingest, linkage, and delivery due to the diverse origins and purposes of the collected data, as well as temporal disparities. In navigating the intricacies of multiple linkages, we will discuss our unique methods, including plans to use an indexing first approach. The first cohort of approximately 10 million children has been created to form the spine and further linkages to the 2011 and 2021 UK census data have been concluded. Linkages between address histories and open environmental data is ongoing. Linkage results have been promising, with high match rate and excellent quality. KEHC is a powerful example of how integrated data can be used for research for the public good. KEHC will provide a unique opportunity for academic researchers investigating the longitudinal effects of local environments on children’s health and educational outcomes and will become one of ADR UK’s flagship Research Ready Datasets.

Linking community-acquired images to national data in the absence of interoperable systems and unique identifiers

Objective &amp; ApproachRoutine optometry examinations, which may include retinal images, have been free to everyone in Scotland since 2006. Images and patient data are stored locally on non-interoperable systems, usually without unique identifiers. The Scottish Collaborative Optometry-Ophthalmology Network for e-research (SCONe) aimed to create a secure, longitudinal repository collating community-acquired images linked to national, routinely-collected healthcare data. We developed bulk export processes to retrieve images and patient data from current and archived systems and a series of deterministic and probabilistic algorithms to match images to patients. ResultsWe successfully exported images and patient data from several commonly used systems. Exact matching of available data between image and clinical systems within practices linked 78% of images (IQR 71.2-84.1). Our algorithms increased match rate to 90% (IQR 83.5-98.6) of images, efficiently reducing risk of systemic bias in the cohort. Match method is included in the repository metadata to facilitate quality assurance checks after national linkage and pseudonymisation. To date over 600k images have been processed and delivered to the National Safe Haven for linkage to healthcare data. Conclusions &amp; ImplicationsBreakthrough research into early detection and pre-symptomatic disease can be facilitated by harnessing community-acquired retinal images which capture normal ageing and disease progression. Rather than await interoperability and widespread use of unique identifiers, this has been achieved through innovative solutions to real-world issues around time-pressured clinical data entry and non-standardised data storage. The SCONe repository continues to grow. It is currently supporting ocular disease prediction research and offers the potential for much more.

Noninvasive visualization of the midline-crossing arterial variation in the deep inferior epigastric artery perforator flap using photoacoustic tomography for application in patients with abdominal scars

BackgroundThe importance of the subcutaneous arterial network crossing the midline in transverse abdominal flaps has been reported. Photoacoustic tomography can be used to noninvasively visualize subcutaneous vascular networks. We applied this novel technology preoperatively in patients undergoing breast reconstruction to detect individual variations in the midline-crossing arteries. MethodsSix patients scheduled to undergo breast reconstruction using free deep inferior epigastric artery perforator flaps were examined. Each scan of the 12×8-cm region took approximately 8min. The accuracy of the tentative arteries evaluation defined by photoacoustic tomography was compared with the arterial phase detected by intraoperative indocyanine green angiography. The number of perforator vessels used for the flap, surgical time for flap elevation, and perfusion area ratio were compared with those of the control group. ResultsThe average match rate between tentative arteries prediction by photoacoustic tomography and arterial-phase assessment by intraoperative angiography in 5 patients was 79.8%. Each midline crossing artery showed individual variations. The photoacoustic tomography group (PAT-1 to 5) showed 1.8 perforators per flap, 163minutes for flap elevation, and 93% perfusion area, with no significant differences from the control group (N=5). A 63-year-old woman (PAT-6) with abdominal scars, including a midline abdominal incision, showed a preserved midline crossing artery. The planned single perforator deep inferior epigastric perforator flap was successfully applied to the contralateral perfusion area. ConclusionsPhotoacoustic tomography noninvasively visualizes the subcutaneous midline-crossing arterial networks. Understanding individual vascular variations can support preoperative planning and surgical indication of abdominal flaps, especially in patients with postsurgical scars.

Error Rates in Race and Ethnicity Designation Across Large Pediatric Health Systems

Without knowledge of the degree of misattribution in racial and ethnic designations in data, studies run the risk of missing existing inequities and disparities and identifying others that do not exist. Further, accuracy of racial and ethnic designations is important to clinical care improvement efforts and health outcomes. To determine the error rate of racial and ethnic attribution in the electronic medical records (EMRs) across the 3 largest pediatric health systems in Michigan. This cross-sectional study collected race and ethnicity data from parents in outpatient clinics, emergency departments, and inpatient units at the 3 largest pediatric health systems in Michigan. A total of 1594 parents or guardians participated at health system A, 1537 at health system B, and 1202 at health system C from September 1, 2023, to January 31, 2024. Parent or guardian report of race and ethnicity for a child was used as the gold standard for comparison with the designation in the EMR. Race and ethnicity designations in the EMR. Options for race designation across the health systems ranged from 6 to 49; options for ethnicity, from 2 to 10. Matching occurred in 3 stages. First, the exact racial and ethnic designations made by parents for their child were compared with what was found in the EMR. Second, for any child whose parent selected more than 1 racial category or for whom more than 1 appeared in the EMR, the designation of a minoritized racial group was used for matching purposes. Third, starting with the product of stage 2, racial designations were combined or collapsed into 6 (health systems A and C) or 5 (health system B) designations. A total of 4333 survey responses were included in the analysis. The greatest error rate across the health systems occurred with the exact match of parental report of racial designation with the EMR, which ranged from 41% to 78% across the health systems. Improvement in the matching rate for each health system occurred with consolidation of race options provided. Differences between the health systems narrowed at the final consolidation to varying from 79% to 88% matching. Ethnicity matching between the EMR and the parental report ranged from 65% to 95% across the health systems. Missing race or ethnicity data in the EMR was counted as a nonmatch. Rates of missing racial data varied across the health systems from 2% to 10%. The health system with the greatest number of options for race and ethnicity had the highest error rates. Although there will always be some misattribution of race and ethnicity in the EMR, the results of this cross-sectional study suggest that significant error in these data may undermine strategies to improve care. It is unclear whether those in an organization who determine the number of potential categories are the same persons who use those data to investigate potential disparities and inequities.