Evaluation of a meta-analysis of air quality and heart attacks, a case study
Bibliographic record
Abstract
It is generally acknowledged that claims from observational studies often fail to replicate. An exploratory study was undertaken to assess the reliability of base studies used in meta-analysis of short-term air quality-myocardial infarction risk and to judge the reliability of statistical evidence from meta-analysis that uses data from observational studies. A highly cited meta-analysis paper examining whether short-term air quality exposure triggers myocardial infarction was evaluated as a case study. The paper considered six air quality components - carbon monoxide, nitrogen dioxide, sulphur dioxide, particulate matter 10 μm and 2.5 μm in diameter (PM10 and PM2.5), and ozone. The number of possible questions and statistical models at issue in each of 34 base papers used were estimated and p-value plots for each of the air components were constructed to evaluate the effect heterogeneity of p-values used from the base papers. Analysis search spaces (number of statistical tests possible) in the base papers were large, median = 12,288 (interquartile range = 2496 − 58,368), in comparison to actual statistical test results presented. Statistical test results taken from the base papers may not provide unbiased measures of effect for meta-analysis. Shapes of p-value plots for the six air components were consistent with the possibility of analysis manipulation to obtain small p-values in several base papers. Results suggest the appearance of heterogeneous, researcher-generated p-values used in the meta-analysis rather than unbiased evidence of real effects for air quality. We conclude that this meta-analysis does not provide reliable evidence for an association of air quality components with myocardial risk.
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How this classification was reachedexpand
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 | 0.031 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads 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".