Letter to the Editor on “Desired decision-making role and treatment satisfaction among trans people during medical transition: results from the ENIGI follow-up study”
Bibliographic record
Abstract
In their recent article, Mayer et al1 reported on the association between transgender patients’ desired decision-making role in gender-affirming care and their treatment satisfaction. We appreciate the authors’ attention to shared decision making in trans healthcare. Respect for the autonomy of trans patients is an essential part of gender-affirming care and is, unfortunately, increasingly subject to political attacks. The authors found high satisfaction toward transition-related interventions across the board but reported a slight negative correlation between desiring an active decision-making role and satisfaction with labia construction (rs = −.275, P = .003, statistically significant) and hormone therapy for patients assigned male at birth (rs = −.160, P = .052, not statistically significant). Discussing these results, the authors suggest that slightly lower satisfaction with hormone therapy among patients who want a more active role in decision making may be due to feeling overwhelmed with options. We do not believe that this interpretation is adequately supported. For this interpretation to be plausible, patients would have had to play a decision-making role that aligns with their preference. Unfortunately, the study did not report the patient’s decision-making role, making it impossible to ascertain whether patients played as active a role as they desired.
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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 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".