The Effect of Single Accreditation on Medical Student Match Rates in Surgical Specialties
Bibliographic record
Abstract
Introduction The year 2020 marked the first year in which a match under single accreditation took place. Both osteopathic (DO) and allopathic (MD) students would participate in the first match cycle without a dedicated DO match system. Our primary objective was to investigate how single accreditation has impacted the DO applicants attempting to match into surgical specialties. Our secondary objective was to investigate the impact of single accreditation at the program director (PD) level and whether or not this process would see a change in DO PD distribution in previously American Osteopathic Association (AOA)-approved programs. Method Information on number of applicants and post-match positions was gathered from AOA and National Residency Match Program (NRMP) websites. Credentials of PDs were obtained from the Accreditation Council on Graduate Medical Education website. Based on the available data, the following surgical specialties were compared for the years 2020, 2018, and 2016: General Surgery, Neurological Surgery (NSGY), Orthopedic Surgery, Otolaryngology/ENT (ENT), Plastic Surgery, and Thoracic Surgery. Data from 2016 were not included in the results as the AOA match results analysis was insufficient and unable to be directly compared to the NRMP data. Results of matched DO and MD applicants were compared using bivariate analysis. A p-value of <0.05 was considered significant. Results From the year 2018 to 2020, the DO applicants saw a decrease of 3% in the total number of matched postgraduate year 1 spots in surgical specialties. NRMP results from 2020 saw that 51.7% of DO applicants matched and 67.7% (p < 0.001) of MD applicants matched for the specialties examined. Percent of matched:applied for DO applicants was lower than MD applicants in the fields of NSGY (p < 0.001), ENT (p < 0.001), Plastic Surgery (p < 0.001), General Surgery (p < 0.001), and Thoracic Surgery (p = 0.011). After evaluating 60 former AOA General Surgery programs, 56% were found to have MD as PD. Another 26 former AOA surgical programs were investigated, and 58% were found to have MD PD. Conclusion Single accreditation has impacted the match process now that a large number of both MD and DO applicants are using the NRMP match system for postgraduate placement. Based on the available data, our results indicate that in the examined surgical specialties, there is a statistically significant difference in the number of MD and DO residents.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".