Historical shift in pathological type of progressive massive fibrosis among coal miners in the USA
Bibliographic record
Abstract
BACKGROUND: Pneumoconiosis among coal miners in the USA has been resurgent over the past two decades, despite modern dust controls and regulatory standards. Previously published studies have suggested that respirable crystalline silica (RCS) is a contributor to this disease resurgence. However, evidence has been primarily indirect, in the form of radiographic features. METHODS: We obtained lung tissue specimens and data from the National Coal Workers' Autopsy Study. We evaluated specimens for the presence of progressive massive fibrosis (PMF) and used histopathological classifications to type these specimens into coal-type, mixed-type and silica-type PMF. Rates of each were compared by birth cohort. Logistic regression was used to assess demographic and mining characteristics associated with silica-type PMF. RESULTS: Of 322 cases found to have PMF, study pathologists characterised 138 (43%) as coal-type, 129 (40%) as mixed-type and 55 (17%) as silica-type PMF. Among earlier birth cohorts, coal-type and mixed-type PMF were more common than silica-type PMF, but their rates declined in later birth cohorts. In contrast, the rate of silica-type PMF did not decline in cases from more recent birth cohorts. More recent year of birth was significantly associated with silica-type PMF. CONCLUSIONS: Our findings demonstrate a shift in PMF types among US coal miners, from a predominance of coal- and mixed-type PMF to a more commonly encountered silica-type PMF. These results are further evidence of the prominent role of RCS in the pathogenesis of pneumoconiosis among contemporary US coal miners.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".