Innovations in MD-only physician-scientist training: experiences from the Burroughs Wellcome Fund physician-scientist institutional award initiative
Bibliographic record
Abstract
IntroductionIn the first half of the 20th century, while the value of translating scientific discovery to clinical care was clearly established, the pace of innovation was resulting in increased specialization in both medicine and science -and increased duration of their respective training paths.In the 1950s and 1960s, American doctors were drafted to serve in the Korean and Vietnam Wars (1).In 1953, the National Institutes of Health (NIH) started its Associate Training Program, which selected physicians who applied to the US Public Health Service Commissioned Corps to serve as clinical associates at the NIH (2).Associates were allowed to pursue mentored research training at the NIH and a unique opportunity for physician-scientist training emerged (3).In 1956, the NIH launched its Experimental Training Program to provide medical students with opportunities for summer research experiences or additional years of training focused on research (4).Such programs have produced leading scientists, including several Nobel Prize laureates, members of the National Academy of Sciences, and directors and institute directors at the NIH (1).Individual medical schools began offering combined MD-PhD training in the 1950s, and in 1964, the National Institute of General Medical Sciences (NIGMS) launched the Medical Scientist Training Program (MSTP) to facilitate MD-PhD dual-degree training programs (4).MSTP graduates are well represented in academia (4); yet, they comprise […]
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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 | 0.057 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 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".