Lost in transition: Addressing the absence of quality surgical outcomes data in gender-affirming surgeries
Bibliographic record
Abstract
Evidenced by previous calls to action in medical journals and in community, 1,2 there is a growing sense of urgency surrounding the need to improve the health and well-being of transgender and gender diverse (trans) patients.One area of trans medicine currently undergoing transformation in Canada is gender-affirming surgeries (GAS), such as vaginoplasty and phalloplasty.Given the expanded availability of GAS in Canada, quality improvement efforts are required for surgeons to innovate surgical techniques and offer patients safer surgeries from a functional and cosmetic perspective.Some trans patients who are transitioning female-to-male, for instance, avoid phalloplasty due to risk of surgical complications. 3 Standardized surgical outcomes data provides surgeons and patients with evidence-informed data, highlighting which procedures have higher and lower rates of complications, resulting in overall quality improvements to GAS, and therefore, less patient hesitancy.Yet robust GAS outcomes data are lacking.4 Canadian GAS outcomes data
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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 | 0.055 | 0.253 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".