Clinical Validation of Self-Measurement for Anogenital Distance in Women
Bibliographic record
Abstract
Abstract Anogenital distance (AGD), the length from the anus to specific genital landmarks, is a well validated, testosterone sensitive, sexually dimorphic biomarker used in many kinds of medical and evolutionary research in diverse species of mammals, including humans. Current research into the effects of testosterone on women’s reproductive health and disease is motivating increased interest in measuring female AGD. Studies quantifying female AGD typically employ a clinician, such as a gynecologist or nurse, to conduct the measurements. This methodology maximizes accuracy but imposes notable limitations on data collection. All participants submitted self-measurements online and completed a small set of questionnaires, including tests assessing spatial cognition. The accuracy of AGD self-measurements, based on agreement between self- and clinic-measurements, was moderate. Measurement accuracy was predicted by performance on the mental rotation test, such that women who performed better on this test demonstrated greater accuracy in measuring the anus to posterior fourchette distance. We describe ideas for improving the accuracy of the self-measurement technique. Self-measurement of AGD would increase the number and diversity of women represented in studies of reproductive health, reduce research expenses, and expedite research into the effects of prenatal testosterone and endocrine disruption on female health and disease.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".