Mapping meta-analyses on organochlorine pesticides reveals low methodological quality
Bibliographic record
Abstract
Abstract Rachel Carson’s Silent Spring inspired a wave of research on the impacts of organochlorine pesticides, followed by a subsequent wave of meta-analyses. However, the methodological quality and content of these meta-analyses has not been evaluated. Here we systematically map and evaluate the methodological quality of 105 meta-analyses on organochlorine pesticides. We found that 83.4% of the evaluated methodological elements are low quality using the Collaboration for Environmental Evidence Synthesis Assessment Tool (CEESAT v2.1). We then reveal that 227 policy documents cited the included meta-analyses, and there is no difference in methodological quality between those that were cited in policy and those that were not. We also found a paucity of meta-analyses on wildlife despite ample primary evidence. Finally, we quantified the positive impact of using reporting guidelines and we provide recommendations for readily implementable methodological improvements.
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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 | 0.382 | 0.777 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.027 | 0.033 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".