Bibliographic record
Abstract
Background": An article in this issue of the Canadian Journal of Neurological Sciences provides a platform for reviewing the role of surgeon-scientists in contemporary medical practice 1 .The article by Fady Girgis is entitled "Feasibility of a dual neurosurgeon-scientist career in Canada: a survey study".The method used to obtain the data was an electronic survey distributed to staff neurosurgeons and neurosurgery residents across Canada.Questions were designed to identify qualities that exemplified ideal neurosurgical practice in relation to research.A neurosurgeon-scientist was defined as a neurosurgeon that spends at least 50% of his or her time performing research or research-related activities.In the case of residents the designation was based on intended practice, rather than current practice.In the results section the author presents the data from 54 neurosurgeons/residents that completed the survey, 32 of whom were current or intended neurosurgeon-scientists.The discussion reviews emergent themes such as: 1. Collaboration in research (85.2% of respondents felt that neurosurgeons who collaborate with basic scientists to conduct research are equally or more likely to obtain meaningful results compared to neurosurgeon-scientist conducting the same research); 2. The 'ideal' versus the 'real' neurosurgeon (83.3% of respondents did not feel that conducting research in addition to clinical duties was needed to become an 'ideal' neurosurgeon); 3. Research training in neurosurgery (85.2% of respondents were in favor of academic centres employing purely clinical neurosurgeons alongside neurosurgeon-scientists, rather than hiring only neurosurgeon-scientists, though 77.8% of respondents reported feeling pressure to publish journal articles).The author concludes that it is possible to do good work in both neurosurgery and neuroscience simultaneously, but in reality it is very difficult to do so."Methods": The author of this editorial performed a PubMed search using the term 'surgeon-scientist'.The articles were reviewed in varying levels of depth according to the information provided in the title and/or abstract.Themes from the articles are reported and discussed using the spirit and the space limitations of an editorial."Results": The PubMed search revealed 163 articles containing the term 'surgeon-scientist' (term 'physicianscientist' identified 777 articles!).Survey of these 163 articles revealed: 1.Many articles that were tributes and/or obituaries to/for eminent surgeons (surgeon-scientists) including the general surgeon Beaumont 2 , the neurosurgeon Rasmussen 3 and the plastic/transplant surgeon Murray 4 ; 2. Many articles that were editorials/opinion pieces/published addresses, including those by Nobel laureates [5][6][7] , presidents of academic surgical societies 8,9 , presidents of scientific societies [10][11][12] , and editors of prominent journals 13,14 ; 3. A smaller number of articles that did gather some data around the topic (surveys/data base extraction), especially as related to training 15,16 and funding 17 of the surgeon-
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 | 0.083 | 0.302 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 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".