Risk of Bias Assessment in Non-Randomized Studies of Interventions for Premenstrual Syndrome: A Systematic Review
Bibliographic record
Abstract
Objective: Premenstrual syndrome (PMS) is a very prevalent condition that affects premenopausal women and can result in monthly debilitating emotional and physical symptoms. The objective of this systematic review was to determine which predictive factors were associated with an increased amount of bias in non-randomized studies (NRSs) of PMS. Materials and methods: A search of the EMBASE and Medline electronic databases was completed from January 1, 2010 to December 2021. The methodological quality of the included studies was independently evaluated and critically appraised using the Risk of Bias in Non-Randomized Studies - of Interventions (ROBINS-1) tool. Associations of different factors with the risk of bias levels were assessed using a univariate logistic regression. Odds ratio and 95% confidence interval (CI) were reported. Results: Of the 1668 studies, 38 were determined to be eligible for inclusion. The ROBINS-1 tool identified that 12 studies were of low/moderate risk of bias (31.6%) and 26 were of serious/critical risk (68.4%). Evidence of relationships between the ROBINS-1 score and impact factor (OR=0.20; 95% CI, 0.07 to 0.57; p= 0.003) and number of authors (OR=0.65; 95% CI, 0.43 to 0.99; p= 0.046) were identified, whereas no relationships were found with the number of citations, the sample size, the funding type, or the conflict-of-interest statement. Conclusion: The systematic review concludes that the methodological rigor of non-randomized studies of PMS can vary, with fewer authors and a lower impact factor showing evidence of association with a decreased quality of evidence.
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 | 0.188 | 0.484 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.025 | 0.030 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".