Validation of autopsy data for epidemiologic studies of coal miners
Bibliographic record
Abstract
BACKGROUND: South Africa has one of the largest miner autopsy databases, PATHAUT, dating back to 1925. The diagnoses recorded on this database have never been evaluated for coal miners. The objective was to determine the validity of the autopsy diagnoses for coal workers, specifically bronchitis, silicosis, tuberculosis, coal workers' pneumoconiosis and emphysema, from 1975 to 1997. METHODS: Three pathologists experienced in miner respiratory pathology conducted the review. They were blinded to employment and medical histories as well as to previous pathological diagnoses on PATHAUT and reviewed 28 coal miners with mixed mining exposures, and 31 cases with exclusive coal mine exposure--all selected randomly. The reviewers' independent and consensus diagnoses were compared to PATHAUT. An additional 31 cases with available whole mount sections were reviewed for the diagnosis of emphysema. Kappa statistics were used to determine degrees of agreement among reviewers and between reviewers and PATHAUT. RESULTS: There was good to excellent agreement between the reviewers and PATHAUT for silicosis, tuberculosis, and pneumoconiosis that had progressed beyond the stage of macules, among the mixed and exclusive coal exposure cases. There was good to excellent inter-reviewer agreement for all diseases except bronchitis (agreement=fair to very good). For emphysema, there was good to very good inter-reviewer agreement but fair agreement with PATHAUT. CONCLUSIONS: This, the first systematic review of PATHAUT autopsy diagnoses made on coal workers, showed that PATHAUT can be used with confidence to establish a diagnosis of moderate to severe grades of coal workers' pneumoconiosis. The grade of emphysema recorded on PATHAUT could be used for epidemiological purposes, when whole mount sections have been prepared.
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 | 0.228 | 0.441 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".