Reproductive surgery: decreasing skills and advancing technology—an existential conundrum
Bibliographic record
Abstract
Our article endeavors to be both a review of the recent past and a preview of the future of reproductive surgery. By reflecting on the rate of technological advancement over the past decade, we attempt to predict the trajectory of the next. We also delve into the changing nature and practical challenges of the practice of gynecologic surgery for the reproductive endocrinology and infertility subspecialist. We will explain how technological advances may alter our perception and expectations regarding the indications, timing and extent of surgical intervention in the infertile patient and in the patient seeking preservation of fertility. This review does not aim to be comprehensive, choosing instead to focus on those innovations that hold, in our view, true potential to shape the future of surgical practice. Ours is primarily a technology review. As such, it does not focus on novel surgical techniques, including uterine transplantation and ovarian tissue transplantation.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".