Surgeon-Scientists Going Extinct
Bibliographic record
Abstract
OBJECTIVE: To define the concept of surgeon-scientists and identify the root causes of their decline in number and impact. The secondary aim was to provide actionable remedies. BACKGROUND: Surgeons who conduct research in addition to patient care are referred to as "surgeon-scientists." While their value to society remains undisputed, their numbers and associated impact have been plunging. While reasons have been well identified along with proposals for countermeasures, their application has largely failed. METHODS: We conducted a systematic review covering all aspects of surgeon-scientists together with a global online survey among 141 young academic surgeons. Using gap analysis, we determined implementation gaps for proposed measures. Then, we developed a comprehensive rescue package. RESULTS: A surgeon-scientist must actively and continuously engage in both patient care and research. Competence in either field must be established through protected training and criteria of excellence, particularly reflecting contribution to innovation. The decline of surgeon-scientists has reached an unprecedented magnitude. Leadership turning hospitals into "profit factories" is one reason, a flawed selection process not exclusively based on excellence is another. Most importantly, the appreciation for the academic mission has vanished. Along with fundamentally addressing these root causes, surgeon-scientists' path to excellence must be streamlined, and their continuous devotion to innovation cherished. CONCLUSIONS: The journey of the surgeon-scientist is at a crossroads. As a society, we either adapt and shift our priorities again towards innovation or capitulate to greed for profit, permanently losing these invaluable professionals. Successful rescue packages must not only involve hospitals and universities but also the political sphere.
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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 | 0.047 | 0.204 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| 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".