Silica, Silicosis, and Lung Cancer: A Response to a Recent Working Group Report
Bibliographic record
Abstract
The relationship between crystalline silica and lung cancer has been the subject of many recent publications, conferences, and regulatory considerations. An influential, international body has determined that there was sufficient evidence to conclude that quartz and cristobalite are carcinogenic in humans. The present authors believe that the results of these studies are inconsistent and, when positive, only weakly positive. Other, methodologically strong, negative studies have not been considered, and several studies viewed as providing evidence supporting the carcinogenicity of silica have significant methodological weaknesses. Silica is not directly genotoxic and is a pulmonary carcinogen only in the rat, a species that seems to be inappropriate for assessing particulate carcinogenesis in humans. Data on humans demonstrate a lack of association between lung cancer and exposure to crystalline silica. Exposure-response relationships have generally not been found. Studies in which silicotic patients were not identified from compensation registries and in which enumeration was complete did not support a causal association between silicosis and lung cancer, which further argues against the carcinogenicity of crystalline silica.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".