Gender expression is associated with selection of uterine preservation or hysterectomy for pelvic organ prolapse surgery: Novel methodology for sex‐ and gender‐based analysis in gynecologic research
Bibliographic record
Abstract
OBJECTIVE: To explore whether patient characteristics were associated with gender expression, and to further determine impact of gender expression on patient selection of hysterectomy or uterine-preservation in pelvic organ prolapse (POP) surgery. METHODS: Within a prospective cohort, a self-reported gender expression tool classified patients as expressing gender polar (i.e., reporting only feminine traits) or non-polar gender scores (i.e., reporting feminine and masculine traits). Multivariate modeling explored associations of gender expression with traditional socio-demographic variables, and with selection of hysterectomy or uterine-preserving surgery. Descriptive statistics of socio-demographic variables were reported by frequency, proportion and mean (SD). RESULTS: 177 participants completed the gender score questionnaire. Overall, the sample had a more feminine gender expression with the majority of respondents classified as gender polar (67.23%, n = 119). Participants with non-polar gender scores were 2.53 times (95% 1.05-6.09) more likely to choose uterine preservation versus hysterectomy-based surgery. Gender polarity was weakly associated with age, but no other sociodemographic variables. CONCLUSION: Gender expression is not tightly associated with socio-demographic variables, and is thus a novel measurement in gynecologic research. Gender polarity appears to be associated with choice to undergo hysterectomy. Further research is required to understand this relationship and implications in clinical outcomes.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".