Fezolinetant impact on health‐related quality of life for vasomotor symptoms due to the menopause: Pooled data from SKYLIGHT 1 and SKYLIGHT 2 randomised controlled trials
Bibliographic record
Abstract
OBJECTIVE: To assess the effect of fezolinetant treatment on health-related quality of life using pooled data from SKYLIGHT 1 and 2 studies. DESIGN: Prespecified pooled analysis. SETTING: USA, Canada, Europe; 2019-2021. POPULATION: 1022 women aged ≥40 to ≤65 years with moderate-to-severe vasomotor symptoms (VMS; minimum average seven hot flushes/day), seeking treatment for VMS. METHODS: Women were randomised to 12-week double-blind treatment with once-daily placebo or fezolinetant 30 or 45 mg. Completers entered a 40-week, active extension (those receiving fezolinetant continued that dose; those receiving placebo re-randomised to fezolinetant received 30 or 45 mg). MAIN OUTCOME MEASURES: Mean changes from baseline to weeks 4 and 12 on Menopause-Specific Quality of Life (MENQoL) total and domain scores, Work Productivity and Activity Impairment questionnaire specific to VMS (WPAI-VMS) domain scores, Patient Global Impression of Change in VMS (PGI-C VMS); percentages achieving PGI-C VMS of 'much better' (PGI-C VMS responders). Mean reduction was estimated using mixed model repeated measures analysis of covariance. RESULTS: Fezolinetant 45 mg mean reduction over placebo in MENQoL total score was -0.57 (95% confidence interval [CI] -0.75 to -0.39) at week 4 and -0.47 (95% CI -0.66 to -0.28) at week 12. Reductions were similar for 30 mg. MENQoL domain scores were also reduced and WPAI-VMS scores improved. Twice as many women receiving fezolinetant reported VMS were 'much better' than placebo based on PGI-C VMS assessment. CONCLUSIONS: Fezolinetant treatment was associated with improvement in overall QoL, measured by MENQoL, and work productivity, measured by WPAI-VMS. A high proportion receiving fezolinetant felt VMS were 'much better' based on PGI-C VMS responder analysis.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.017 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".