Bibliographic record
Abstract
oag and colleagues highlight a growing concern among Canadian surgical training programs: minimally invasive approaches (laparoscopic and robotic) continue to displace open surgical experience of our urology trainees.1,2 It seems just a short time ago that residents commonly expressed concerns regarding a lack of experience in minimally invasive surgery (MIS).The presentday-urology resident is now faced with the exact opposite situation.3,4 I commonly field concerns from our own residents in Winnipeg regarding their anxiety over lack of experience in "open" cases.Our chief residents have become opportunists, often sending a junior resident to attend my MIS case, while they jump at the opportunity to join a rare open nephrectomy or prostatectomy!Obviously, open surgical skills still remain vital.As pointed out by Hoag and colleagues, open skills are needed for cases not amendable to MIS or in situations where conversion to open is required.1 Organ procurement provides excellent exposure to anatomy throughout the pelvis, abdomen and retroperitoneum; 5 all of which are relevant to various urology procedures.This makes organ procurement a potential solution to aid our current lack of open surgical experience for urology residents.However, there are several limitations unique to organ procurement that may limit this concept.Firstly, procurement procedures often occur after hours, late at night, when residents are likely busy with call-related duties.Secondly, as pointed out by the authors, not all training programs in Canada have a transplant program and hence exposure to donors at these locations will be severely limited.Thirdly, a 'transplant' team comprised of fellows, many of which travel
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".