Initial Match Rates of an Innovative International Partnership: The Ochsner Clinical School Experience.
Bibliographic record
Abstract
BACKGROUND: Ochsner Clinical School (OCS) is a unique partnership between Ochsner Health System in New Orleans, LA, and The University of Queensland (UQ) School of Medicine in Brisbane, Australia. OCS trains physicians in global medicine and promotes careers in primary care through its unique structure. The purpose of this study was to determine how OCS graduates perform in the National Resident Matching Program (NRMP)-The Match-compared to applicants from other types of medical schools. METHODS: The match outcomes for all OCS graduates since the first graduating class in November 2012 were compared to the match outcomes in the NRMP database for graduates from other types of medical schools in the years 2013-2015. We also examined the number of OCS students electing residencies in primary care compared to the number of US medical school graduates overall during the same time period of 2013-2015. RESULTS: The cumulative match rate from 2013-2015 for applicants from OCS was 91.8%. The OCS graduates' match rate was greater than the match rate for US citizen graduates of international medical schools during the same period (53.0% vs 91.8% [z=6.066, P<0.0002]), greater than the match rate for applicants from US osteopathic medical schools (77.3% vs 91.8% [z=25.233, P<0.0002]), and greater than the match rate for applicants from Canadian medical schools (62.7% vs 91.8% [z=3.815, P<0.0002]). The OCS match rate was not significantly different from that of US medical school graduates: 94.0% vs 91.8% (z=-0.728, P=0.4666). During the 2013-2015 time frame, 44.3% of OCS graduates chose residencies in primary care fields compared to 38.3% of US graduates (z=-0.9634, P=0.337). CONCLUSION: Graduates of OCS are obtaining residency positions through The Match at rates comparable to those of US medical school graduates and at rates significantly greater than other groups, and we are seeing a trend in the number of graduates choosing careers in primary care.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".