Physician Scientists Of Yesterday, Today And Tomorrow
Bibliographic record
Abstract
While the separate roles of physicians and scientists are well defined, the role of a physician scientist is broad and variable. In today’s society, physician scientists are seen as a hybrid between the two fields and they are, therefore, expected to be key to the translation of biomedical research into clinical care. This article offers a narrative review on physician scientists and endeavours to answer whether there is an ongoing need for physician scientists today. The historical role of physician scientists is discussed and compared with physician scientists of the 21st century. Fundamental differences and similarities between the separate roles of physicians and scientists are examined as well as the current state of bench to bedside research. Finally, the ability of 21st century physician scientists to impact their respective medical and scientific fields in comparison to non-physician scientists will be discussed. This paper speculates as to why numbers of physician scientists are dwindling and uses the COVID-19 pandemic as an example of rapid translational research. Ultimately, we suggest that physician scientists are important and may have the most impact on their field by working to connect bedside and bench rather than simply working separately in the bedside and bench. To do this, physician scientists may need to lead clinical research teams composed of individuals from diverse training backgrounds.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.015 | 0.024 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".