Meta-analysis of silicosis and lung cancer
Bibliographic record
Abstract
OBJECTIVES: This study examined the association between silicosis and lung cancer in a systematic review (and meta-analysis) of the epidemiologic literature, with special reference to the methodological quality of observational studies. METHODS: We searched Medline, Toxline, BIOSIS and Embase (1966-May 2004) for original articles published in any language and systematically reviewed the bibliographies of the retrieved articles. Observational studies (cohort and case-control studies) were selected if they reported a measure of association [standardized mortality ratio (SMR), relative risk or odds ratio] relating lung cancer to silicosis. RESULTS: Thirty-one studies (27 cohort studies, 4 case-control studies) met the inclusion criteria of the meta-analysis. Without any adjustment for smoking, the meta-analysis of the cohort studies indicated that the common SMR was 2.45 [95% confidence interval (95% CI) 1.63-3.66; homogeneity P<0.0001]. When the results of the cohorts for which mortality data were adjusted for smoking were pooled, the common SMR was 1.60 (95% CI 1.33-1.93; homogeneity P=0.52). In a "dose-response" analysis, the profusion of small and large opacities found in chest X-rays correlated with the risk of death from lung cancer. Overall, the case-control studies were more conservative in their conclusions. CONCLUSIONS: Because of biases inherent to observational studies, it is likely that the risk of lung cancer among silicosis patients is overestimated in the current literature. There is nevertheless evidence, from data restricted to never-smokers and from a "dose-response" analysis, that silicosis and lung cancer are associated.
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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 | 0.029 | 0.054 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.044 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".