Quality of Evidence Must Guide Risk Assessment of Asbestos, by Lenters, V; Burdorf, A; Vermeulen, R; Stayner, L; Heederik, D
Bibliographic record
Abstract
In 2011, Lenters et al. (2011) published a meta-analysis of the relationship between asbestos exposure and consequent lung cancer risk. In 2012, this journal published a critique (Berman and Case, 2012) of the meta-analysis, together with a response (Lenters et al., 2012) by (most of) the original authors. This letter examines some statistical issues raised in this exchange, and suggests that the main finding of the original meta-analysis is less robust than claimed, and should be interpreted cautiously. Lenters et al. performed their calculations in SAS, using a random effects approach and estimating between study variance using restricted maximum likelihood. The present calculations are made on the same basis, but using the R package ‘metafor’ (Viechtbauer, 2010). The central claim of the original meta-analysis was that ‘studies with higher quality asbestos exposure assessment yield higher meta-estimates of the lung cancer risk per unit of exposure’, and the essence of Berman and Case’s critique was that this conclusion was based on estimates that were in many cases not conventionally statistically significant, and were additionally over-reliant on the findings from 1 of the 19 studies examined. Table 1 of Lenters et al. (2011) lists the studies, and this letter uses the numbering given there.
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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 | 0.283 | 0.702 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.025 | 0.017 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.015 | 0.030 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".