Meta‐analyses in systematic reviews of randomized controlled trials in perinatal medicine: comparison of fixed and random effects models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There is a need for empirical work comparing the random effects model with the fixed effects model in the calculation of a pooled relative risk in the meta-analysis in systematic reviews of randomized controlled trials. Such comparisons are particularly important when trial results are heterogeneous. We considered 84 independent meta-analyses in which each trial included a set of different women/newborns. These meta-analyses were included in systematic reviews published in the Cochrane Library's pregnancy and childbirth module. Twenty-one of these 84 meta-analyses demonstrated statistical heterogeneity at p<0.10. The random effects model estimates showed wider confidence intervals, particularly in those meta-analyses showing heterogeneity in the trial results. The summary relative risk for the random effects model tended to show a larger protective treatment effect than the fixed effects model in the heterogeneous meta-analyses. In this set of meta-analyses, statistical evaluation of publication bias cannot be shown to account for heterogeneity. Our empirical conclusion is that there may be opposing effects if the random effects model is used in the meta-analysis of clinical trials showing heterogeneity in the results: stronger treatment effects reflected in the summary relative risk, but wider confidence intervals about this summary measure.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.600 | 0.761 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.105 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it