Reliability of a Meta-analysis of Air Quality−Asthma Cohort Studies
Bibliographic record
Abstract
What may be a contributing cause of the replication problem in science – multiple testing bias – was examined in this study. Independent analysis was performed on a meta-analysis of cohort studies associating ambient exposure to nitrogen dioxide (NO2) and fine particulate matter (PM2.5) with development of asthma. Statistical tests used in 19 base papers from the meta-analysis were counted. Test statistics and confidence intervals from the base papers used for meta-analysis were converted to p-values. A combined p-value plot for NO2 and PM2.5 was constructed to evaluate the effect heterogeneity of the p-values. Large numbers of statistical tests were estimated in the 19 base papers – median 13,824 (interquartile range 1,536−221,184). Given these numbers, there is little assurance that test statistics used from the base papers for meta-analysis are unbiased. The p-value plot of test statistics showed a two-component mixture. The shape of the p-value plot for NO2 suggests the use of questionable research practices related to small p-values in some of the cohort studies. All p-values for PM2.5 fall on a 45-degree line in the p-value plot indicating randomness. The claim that ambient exposure to NO2 and PM2.5 is associated with development of asthma is not supported by our analysis.
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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 | 0.746 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.034 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".