Sexual Desire and the Female Sexual Function Index (FSFI): A Sexual Desire Cutpoint for Clinical Interpretation of the FSFI in Women with and without Hypoactive Sexual Desire Disorder
Bibliographic record
Abstract
INTRODUCTION: A validated cutpoint for the total Female Sexual Function Index scale score exists to classify women with and without sexual dysfunction. However, there is no sexual desire (SD) domain-specific cutpoint for assessing the presence of diminished desire in women with or without a sexual desire problem. AIMS: This article defines and validates a specific cutpoint on the SD domain for differentiating women with and without hypoactive sexual desire disorder (HSDD). METHODS: Eight datasets (618 women) were included in the development dataset. Four independent datasets (892 women) were used in the validation portion of the study. MAIN OUTCOME MEASURES: Diagnosis of HSDD was clinician-derived. Receiver-operator characteristic (ROC) curves were used to develop the cutpoint, which was confirmed in the validation dataset. RESULTS: The use of a diagnostic cutpoint for classifying women with SD scores of 5 or less on the SD domain as having HSDD and those with SD scores of 6 or more as not having HSDD maximized diagnostic sensitivity and specificity. In the development sample, the sensitivity and specificity for predicting HSDD (with or without other conditions) were 75% and 84%, respectively, and the corresponding sensitivity and specificity in the validation sample were 92% and 89%, respectively. CONCLUSIONS: These analyses support the diagnostic accuracy of the SD domain for use in future observational studies and clinical trials of HSDD.
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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 | 0.004 | 0.013 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".