Bibliographic record
Abstract
The paper by Thomsen et al 1 is a significant contribution to the now-substantial body of literature on the relationship between asbestos-related diseases and vehicle repair work. It is a large study, inclusive of all registered vehicle mechanics in the country for the designated years, with a mean follow-up time of 20 years and a maximum of 45 years. Several comprehensive administrative databases were linked for exposure and disease outcomes. The numbers of outcomes for the three main diseases of interest produced relatively narrow CIs. The authors present data indicating that reliance on administrative databases for health outcomes was unlikely to be a problem for mesothelioma and lung cancer; however, it is reasonable to question the validity of the diagnoses of asbestosis based on administrative data alone. As the authors point out, the statistically significant elevation in asbestosis mortality and morbidity is somewhat puzzling given the relatively high exposures required for asbestosis and the relatively low asbestos exposures experienced by vehicle mechanics. The existing literature does not support a positive relationship between asbestosis and vehicle repair work.2–5 The authors suggested that diagnostic bias may have played a role, but this is speculative. The result warrants further examination, possibly a nested case–control study with documentation of the diagnostic criteria supporting the database entries, blinded re-evaluation of the available diagnostic material, and further exploration of complete occupational histories. The investigators observed a slight elevation in lung cancer risk. The results are consistent with those from an earlier meta-analysis6 and are unremarkable. Studies of potential asbestos-related diseases among vehicle mechanics have focused primarily on risk of …
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".