Bibliographic record
Abstract
Doing surgeryT o do surgery you must be a surgeon.Sounds obvious, but some of our provincial governing bodies don't agree and they have a dilemma.They think a physician should be able to change their scope of practice.That laudable idea has problems.Who decides how much training is enough to perform a few operations?How do you find a surgeon training assessor if surgeons are opposed to the idea of a physician trained to do a few operations only?A surgeon is broadly trained.It takes five or more years after the MD, then rigorous examinations.Only then do surgeons subspecialize.This method of training is over 60 years old and replaced the physician who was both physician and surgeon -the family doctor who did occasional surgery.Is there a shortcut to surgery?Is it smart to train a physician to do a few operations?This is an attractive idea to some.It looks progressive, is certainly shorter and might be cheaper.It gives physicians opportunities in an era where many things are restricted and centrally controlled.We disagree.The best way to surgery is the difficult way.It takes five years after the MD degree to become a surgeon.This broad training remarkably benefits the patient because the surgeon has a wide knowledge of many surgical disciplines.The training is in performing the operation, in preoperative assessment, the consent and especially in the diagnosis and treatment of postoperative complications.Some physicians would prefer a shortcut to surgical training.No shortcut exists.If a physician wants to do surgery, become a surgeon.Join us! Find a good training program and work hard.These training positions are limited.To get accepted, the candidate needs to be patient and convince the powers that be that you would be a good surgeon.Your marks will have to be high, so study hard.Then you can begin an exciting surgical life.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.424 | 0.277 |
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".