Reliability and Convergent Validity of the Algometer for Vestibular Pain Assessment in Women with Provoked Vestibulodynia
Bibliographic record
Abstract
OBJECTIVE: Women with provoked vestibulodynia (PVD) suffer pain at the entry of the vagina elicited by pressure as during vaginal penetration. To quantify vestibular pain, we developed a new instrument, an algometer. The aim of this study was to investigate the test-retest reliability of the algometer and evaluate its convergent validity for vestibular pain assessment in women with PVD. METHODS: Twenty-six women with PVD participated in the study. Vestibular pain was assessed with the new algometer and the already known vulvalgesiometer during two different sessions 2 to 4 weeks apart. At each session, the pressure pain threshold (PPT) and pressure pain tolerance (PPTol) were measured twice at the 3, 6, and 9 o'clock sites of the vestibule in random order. The test-retest reliability (intra- and inter-session) of the algometer was calculated using the intraclass correlation coefficient (ICC) and standard error of measurement (SEM). Its convergent validity was evaluated by the correlation coefficients between PPTs and PPTols measured by the algometer and those measured with the vulvalgesiometer. RESULTS: Intra-session reliability at all three sites for PPTs and PPTols in both sessions was excellent (ICC = 0.859 to 0.988, P ≤ 0.002). Inter-session reliability was good to excellent (ICC = 0.683 to 0.922, SEM = 15.06 to 47.04 g, P ≤ 0.001). Significant correlations were found between the two tools for all sites for PPTs (r = 0.500 to 0.614, P ≤ 0.009) and PPTols (r = 0.809 to 0.842, P < 0.001). DISCUSSION: Findings showed that the algometer is a reliable and valid instrument for measuring PPTs and PPTols in the vestibular area in women with PVD. This technology is promising for pinpointing treatment mechanisms and efficacy.
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".