Statistical Reliability of a Diet-Disease Association Meta-analysis
Bibliographic record
Abstract
Risk ratios or p-values from multiple, independent studies – observational or randomized – can be pooled to address a common research question in meta-analysis. However, reliability of independent studies should not be assumed as claimed risk factor−disease relationships may fail to reproduce. An independent evaluation was undertaken of a published meta-analysis of cohort studies examining diet−disease associations; specifically between red and processed meat and six disease outcomes (all-cause mortality, cardiovascular mortality, all cancer mortality, breast cancer incidence, colorectal cancer incidence, type 2 diabetes incidence). The number of hypotheses examined were counted in 15 random base papers (14%) of 105 used in the meta-analysis. Test statistics (relative risk values with 95% confidence limits) for 125 results used in the meta-analysis were converted to p-values; p-value plots were used to examine the effect heterogeneity of the p-values. The possible number of hypotheses examined in the 15 base papers was large, median = 20,736 (interquartile range = 1,728–331,776). Each p-value plot for selected health effects showed either a random pattern (p-values > 0.05), or a two-component mixture (small p-values < 0.001 while other p-values appeared random). Given potentially large numbers of hypotheses examined in the base studies, questionable research practices cannot be ruled out as explanations for some test statistics with small p-values. Like the original findings of the published meta-analysis, our independent evaluation concludes that base papers used in the meta-analysis do not support evidence for an association between red and processed meat and the six health effects investigated.
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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 | 0.495 | 0.681 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.024 | 0.068 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".