Statistical Reproducibility of Meta-Analysis for Medical Mask Use in Community Settings to Prevent Airborne Respiratory Virus Infection
Bibliographic record
Abstract
Many US states, cities, and counties implemented public masking orders during the coronavirus (COVID) pandemic on the notion that this intervention would delay and flatten the epidemic peak and largely benefit public health outcomes. A p-value plot can provide insights into possible inappropriateness (incorrectness) of assumptions of a statistical model. It can be used to confirm, disprove, or identify ambiguity (uncertainty) in a meta-analytic finding and research claim. P-value plotting was used to evaluate statistical reproducibility of meta-analysis studies for disposable medical (surgical) mask use in community settings to prevent airborne respiratory virus infection. Eight studies (seven meta-analysis, one systematic review) published between 1 January 2020 and 7 December 2022 were evaluated. Base studies were randomized control trials with outcomes of medical diagnosis or laboratory-confirmed diagnosis of viral (Influenza or COVID) illness. Self-reported viral illness outcomes were excluded from the evaluation because of awareness bias. No evidence was observed for a medical mask benefit to prevent respiratory virus infection in six p-value plots (five meta-analysis and one systematic review). Research claims of no benefit in three meta-analysis and the systematic review were reproduced in p-value plots. Research claims of a benefit in two other meta-analysis were not reproduced in p-value plots suggesting irreproducibility of these claims. Insufficient data was available to construct p-value plots for two other meta-analysis because of over-reliance on self-reported outcomes. Independent findings of p-value plotting show that meta-analysis of existing randomized control trials fail to demonstrate a benefit of medical mask use in community settings to prevent airborne respiratory virus infection.
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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 | 0.237 | 0.449 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.070 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| 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; 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".