Psychological Intervention for Premenstrual Syndrome: A Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND: We conducted a systematic review and meta-analysis to determine the efficacy of psychological interventions for premenstrual syndrome. METHODS: We systematically searched and selected studies that enrolled women with premenstrual syndrome in which investigators randomly assigned them to a psychological intervention or to a control intervention. Trials were included irrespective of their outcomes and, when possible, we conducted meta-analyses. RESULTS: Nine randomized trials, of which 5 tested cognitive behavioural therapy, contributed data to the meta-analyses. Low quality evidence (design and implementation weaknesses of the studies, possible reporting bias) suggests that cognitive behavioural therapy significantly reduces both anxiety (effect size [ES] = -0.58; 95% confidence interval [CI] = -1.15 to -0.01; number needed to treat [NNT] = 5), and depression (ES = -0.55; 95% CI = -1.05 to -0.05; NNT = 5), and also suggests a possible beneficial effect on behavioural changes (ES = -0.70; 95% CI = -1.29 to -0.10; NNT = 4) and interference of symptoms on daily living (ES = -0.78; 95% CI = -1.53 to -0.03; NNT = 4). Results provide much more limited support for monitoring as a form of therapy and suggest the ineffectiveness of education. CONCLUSIONS: Low quality evidence from randomized trials suggests that cognitive behavioural therapy may have important beneficial effects in managing symptoms associated with premenstrual syndrome.
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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 | 0.029 | 0.063 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.048 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".