Comparing and combining evidence of treatment effects in randomized and nonrandomized studies on the use of misoprostol to prevent postpartum hemorrhage
Bibliographic record
Abstract
OBJECTIVE: Postpartum hemorrhage (PPH) is a preventable condition and the main cause of maternal death worldwide. Evidence on the effectiveness of misoprostol in the prevention of PPH has been generated from both randomized controlled trials (RCTs) and nonrandomized studies (NRS). This study aimed to compare the results of RCTs and NRS, and to compare Classical and Bayesian approaches of combining the results of RCTs and NRS on the use of misoprostol versus placebo in the prevention of PPH. METHODS: We searched MEDLINE, EMBASE and the Cochrane Central Register of Controlled Trials for appropriate studies. We pooled estimates of effects from RCTs and NRS seperately, using random-effects models, then merged them using classical and Bayesian random effects meta-analysis. RESULTS: A total of 34 studies (20 RCTs and 14 NRS) involving 74 204 participants were identified. The summary odds ratio (OR) from RCTs for the use of misoprostol in the prevention of PPH was 0.69 (95% confidence interval [CI]: 0.59 to 0.80). The summary OR from NRS was 0.46 (95% CI: 0.36 to 0.63). Classical and Bayesian approaches of combining the two study designs both showed benefit of misoprostol in preventing PPH, with similar effects. CONCLUSIONS: Both RCTs and NRS show comparable significant benefit for the use of misoprostol in the prevention of PPH.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
| gpt | Meta-epidemiology (narrow)Meta-epidemiology (broad)Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
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 | 0.218 | 0.480 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.026 | 0.046 |
| Bibliometrics | 0.017 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".