Conclusive meta‐analyses on antenatal magnesium may be inconclusive! Are we underestimating the risk of random error?
Bibliographic record
Abstract
Results from meta-analyses significantly influence clinical practice. Both simulation and empirical studies have demonstrated that the risk of random error (i.e. spurious chance findings) in meta-analyses is much higher than previously anticipated. Hence, authors and users of systematic reviews and meta-analyses have a responsibility to carefully consider the risk of random errors to avoid misleading conclusions. Trial sequential analysis is a useful meta-analytic method for gauging the risk of random error in meta-analysis and the amount of additional evidence required to reach firm conclusions about the investigated intervention effect(s). We outline the rationale for conducting trial sequential analysis including some examples of the meta-analysis on antenatal magnesium for women at risk of preterm birth.
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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 | 0.224 | 0.683 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.032 | 0.036 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".