Bibliographic record
Abstract
PURPOSE OF REVIEW: Sleep problems are among the most prevalent and bothersome symptoms of menopause. This review characterizes menopausal sleep disturbances, describes biopsychosocial predictors, and summarizes the evidence supporting pharmacological and nonpharmacological treatment options. RECENT FINDINGS: Recent studies found that sleep changes are early indicators of perimenopause and sought to disentangle the respective impacts of menopausal status, hot flashes (HFs), and changes in reproductive hormones on peri-/postmenopausal sleep problems. Both HFs and reproductive hormones predicted sleep problems, but neither solely accounted for the myriad changes in sleep, thus highlighting the contribution of additional biopsychosocial risk factors. Inconsistencies across studies were likely due to differences in study design and methodology, participants' menopausal stage, and the presence of sleep complaints. Recent studies support the use of psychological (cognitive-behavioral therapy for insomnia) and pharmacological (e.g., neurokinin B antagonists) treatments in addition to hormone therapy. SUMMARY: Sleep problems are common and of critical import to women during the menopausal transition, significantly influencing treatment preferences and satisfaction. Thus, sleep problems should be routinely assessed from a biopsychosocial perspective and treated with evidence-based interventions throughout menopause. Treatment selection should be based on diagnosis and careful assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".