Letter to the Editor: GENDER-Q Youth will measure outcomes that matter to youth seeking gender-affirming care
Bibliographic record
Abstract
Dear Editor, With the rising demand for gender-affirming care, especially amongst transgender and gender-diverse (TGD) youth, there is a critical need for tools that can be used to accurately measure outcomes. Gender-affirming treatment can change how the voice sounds and how the face, body and genitals look and function. Since many of the outcomes associated with gender-affirming treatments affect how patients function and feel, rigorously developed, comprehensive gender-specific patient-reported outcome measures (PROMs) are urgently needed. In fact, the development of PROMs that measure outcomes important to TGD adults and youth in a way that is reliable, valid and reproducible was called for by the World Professional Association for Transgender Health board in an open letter dated May 2018 (1). In addition, several systematic reviews of PROMs used in TGD research have converged to conclude that most existing PROMs are inappropriate since they tend to be generic (designed for use in any health condition), designed for cisgender populations, and designed without adherence to internationally recommended guidelines for the development of PROMs (2–7). The only exception to the call for gender-specific PROMs seems to be that of Jackman and colleagues in their review published in July 2024 (8). Jackman et al. found three PROMs measuring gender-related concepts for youth (in fact, only one PROM met the review inclusion criteria as two were not supported by peer-reviewed publications). Jackman et al.’s solution to the lack of gender-specific PROMs for youth is to suggest using the 35 PROMs they found that measure psychological function and quality of life. This recommendation reflects their view that development of new gender-specific PROMs is time and resource-intensive and potential research waste.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.053 | 0.042 |
| Insufficient payload (model declined to judge) | 0.009 | 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".