The Montreal Criteria and uterine transplants in transgender women
Bibliographic record
Abstract
Ever since its first documented live birth in 2014, the use of uterine transplantation (UTx) for the treatment of absolute uterine factor infertility (UFI) has seen major clinical advances, which include the use of alternative surgical approaches, different donor states, and diverse patient populations. In addition to the thorough research programs that developed the technique, this accomplishment has occurred in large part following a number of ethical frameworks, such as the Montreal Criteria and the Indianapolis Consensus, which paved the way to transition from experimental animal trials to human ones. To date, over 60 uterine transplants have been performed in the world, and at least 18 births have been thus far confirmed. While the procedure remains experimental, the vast knowledge and procedural experience amassed over the last 20 years of rigorous research have hinted at the next step of discovery. In particular, advancing social circumstances have prompted the question regarding the use of this technology in transgender individuals. Though the potential use of uterine transplants in the transgender population has been hypothesized, no in-depth ethical framework has been developed towards this purpose. Herein, we explore the ethical issues revolving around the use of this technology in this patient population and provide key insights that may advance this cause.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".