Malignant mesothelioma incidence among talc miners and millers in New York State
Bibliographic record
Abstract
BACKGROUND: There is controversy about the potential for dust from the talc mines and mills of New York State to cause mesothelioma. Honda et al. published a study of mortality among New York talc workers and concluded that it was unlikely that the two deaths from mesothelioma were caused by talc ore dust. However, fibers of tremolite and anthophyllite have been found in the lungs of talc workers and Hull concluded that "New York talc exposure is associated with mesothelioma, and deserves further public health attention." METHODS: Data concerning additional cases of mesothelioma in the cohort have been posted by NIOSH. I used information from the NIOSH website and the Honda report to analyze the incidence of mesothelioma during the years 1990-2007. RESULTS: There were at least five new cases of mesothelioma in the cohort and mesothelioma incidence rates were at least five (1.6-11.7) times the rate in the general population (P < 0.01). CONCLUSIONS: I conclude that: (1) mesothelioma has been diagnosed among members of the cohort at a rate in excess of that in the general population; (2) fibers of tremolite and anthophyllite have been detected in dust and the lungs of talc workers; and (3) these fibers are known causes of mesothelioma. It is prudent, on the balance of probabilities, to conclude that dusts from New York State talc ores are capable of causing mesothelioma in exposed individuals.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".