The making of a physician-scientist--the process has a pattern: lessons from the lives of Nobel laureates in medicine and physiology
Bibliographic record
Abstract
Physician-scientists are catalysts of translational research. With one foot in the practice of medicine and the other in research and discovery, they are uniquely positioned to bridge the gap between laboratory and bedside. In so doing, they enhance patient care, improve medical education, and increase the prosperity of the biomedical enterprise. Although, science has never been more accessible and directly applicable to human health, there is a paradoxical scarcity of physician-scientists. Causes of this shortage include prolonged training and the associated debt-load, the corporatization of medicine, inadequate research funding, and the complexity of a dual career. While striving to reduce these obstacles, we should inspire the next generation by celebrating the physician-scientist career track as one of Medicine's most rewarding. To this end, life lessons from five groups of Nobel laureates in medicine and physiology have been distilled, revealing the essence of the practices and philosophies that allowed these 'ordinary' people to achieve the extraordinary. The common threads in their stories guide young physician-scientists to seek out training and employment where a culture of research is embraced, to find a dedicated mentor who will help identify worthy research questions and guide their career, and to establish research partnerships which offer creative synergy and buffer the frustrations that accompany research. Further inspiration comes from those great researchers whose contributions shaped Medicine but did not lead to the Prize.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.084 |
| Scholarly communication | 0.031 | 0.035 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.009 | 0.033 |
| Insufficient payload (model declined to judge) | 0.004 | 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".