Evaluation of the Decision Aid for Genital Surgery in Transmen
Bibliographic record
Abstract
BACKGROUND: Multiple options of genital gender-affirming surgery are available to transmen. The transman should be able to weigh these options based on the outcomes, risks, and consequences that are most important to him. For this reason, a decision aid for genital surgery in transmen (DA-GST) was developed. It aims to support the transman in making thoughtful choices among treatment options and facilitate shared decision-making between the healthcare professionals and the transindividual. AIM: The aim of this study was to evaluate the newly developed DA-GST. METHODS: This was a cross-sectional study using mixed methods. Transmen considering to undergo genital surgery were eligible to partake in the study. The questionnaires used in this study were developed by adapting the validated Dutch translation of the "Decisional Conflict Scale," the "Measures of Informed Choice," and the "Ottawa Preparation for Decision-Making Scale." Qualitative interviews were conducted querying their subjective experience with the DA-GST. The data from the questionnaires were statistically analyzed, and the data from the interviews were thematically analyzed. OUTCOMES: The main outcome measures were decisional conflict and decisional confidence measured via self-report items and qualitative data regarding the use of the DA-GST via interviews. RESULTS: In total, 51 transmen participated in the questionnaires study, 99 questionnaires were analyzed, and 15 interviews were conducted. Although confident in their decision, most transmen felt responsible to collect the necessary information themselves. The ability to go through the decision aid independently aided the decision-making process by providing information and highlighting their subjective priorities. Suggested additions are pictures of postoperational outcomes and personal statements from experienced transmen. CLINICAL TRANSLATION: The DA-GST could be implemented as an integral part of transgender health care. Clinicians could take the individual personal values into account and use it to accurately tailor their consult. This would ultimately improve the doctor-patient relationship and decrease decisional regret by enhancing effective shared decision-making. STRENGTHS & LIMITATIONS: This mixed-method design study confirmed the use of the DA-GST while taking a broad range of decisional factors into account. Limitations include the absence of a baseline analysis and the limited power for the comparison of treatment groups. CONCLUSIONS: This study suggests that the DA-GST helped transmen feel more prepared for their personal consult with the surgeon, reduced decisional conflict, and increased their decisional confidence. Mokken SE, Özer M, van de Grift TC, et al. Evaluation of the Decision Aid for Genital Surgery in Transmen. J Sex Med 2020;17:2067-2076.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".