Bibliographic record
Abstract
There is sufficient understanding of the causation of occupational asthma for preventive action to be appropriate. To date, attempts appear to have been largely unsuccessful and this appears to be largely due to nonscientific/technical obstacles. These include the fragmented nature of the disease, its low public and industrial profile, and its comparative rarity in single workplaces. Nonetheless the disease has high individual and societal costs. Prevention strategies should be concentrated on workplace-exposure controls, accompanied by intense educational and managerial improvements. Methods of secondary prevention appear to be successful but require considerable refinement. Screening (out) of potential new employees is inefficient and likely to remain so; and in any case is beset by difficult ethical and legal issues. There are only a handful of published studies reporting evaluations of preventive programmes. None is entirely rigorous but each suggests that primary and secondary prevention are both feasible and highly effective. The evaluation of preventive strategies is difficult, not only because of the low incidence of the disease in individual workplaces but also because of the failure of many epidemiologists to engage in this work. Considerably more cooperation between scientists in the field, regulatory authorities and industry is required.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".