Methodological quality of antimalarial randomized controlled trials during pregnancy and its impact on the risk of low birth weight
Bibliographic record
Abstract
AIM: To describe biases in antimalarial randomized controlled trials (RCTs) during pregnancy and their influence on antimalarial drug efficacy to reduce the risk of low birth weight (LBW). METHODS: RCT characteristics and results were retrieved from a previous systematic review on the efficacy of antimalarials. The Cochrane risk of bias assessment was used to investigate source of biases in each RCT. The quality of RCT reporting published after the introduction of the CONSORT statement in medical literature in 1996 were compared to those published before 1996. A meta-regression analysis was performed to examine the impact of bias on the efficacy of antimalarials to reduce LBW after controlling for the time period prior to 1996. RESULTS: Twenty out of 25 RCTs (80%) had a high risk of bias. The proportion of RCTs having a low risk of bias was higher in manuscripts published after the introduction of CONSORT compared to those published before 1996 for sequence generation (P = 0.04) and allocation concealment (P = 0.04). Heterogeneity between RCTs was associated with an overestimation of the efficacy of antimalarial drugs in reducing LBW in RCTs with inadequate methods for randomization, allocation concealment or not being free of other bias. CONCLUSION: Antimalarial RCTs during pregnancy are poorly reported but may be improved by using the CONSORT statement. After taking into account the time period before 1996, we found that biases had an impact on the efficacy of antimalarials to reduce the risk of LBW.
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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 | 0.496 | 0.756 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.019 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| 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 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".