From bench to bedside: a call to expand physician pathways for PhDs
Bibliographic record
Abstract
There are about 200 accredited medical schools in the United States. Among these, about 160 are allopathic (MD) and nearly 40 schools are osteopathic (DO). Collectively, these schools graduate over 28,000 physicians each year. In addition, over 75% of the MD schools have MD/PhD programs that train physician-scientists. Despite these relentless efforts to prepare physicians to become scientists who comprehensively understand the molecular basis of diseases and facilitate drug discovery and development efforts, there remains a notable shortage of physician-scientists. Although training established PhD-level scientists to become physicians is an attractive strategy to mitigate the shortage, there doesn't appear to be a well-defined path that trains PhDs to earn their medical degree. This problem is even more daunting for PhDs who trained outside the United States or Canada. This review highlights the advantages of training established biomedical scientists to become physicians and makes a case for medical schools to launch PhD-to-MD or PhD-to-DO programs to equip these scientists with clinical acumen to help bridge the widening gap between basic science research and clinical care as well as to mitigate our heavy and unsustainable reliance on international medical graduates to supply our medical workforce.
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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 | 0.046 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.015 | 0.027 |
| Insufficient payload (model declined to judge) | 0.048 | 0.014 |
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".