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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it