Evidence based guidelines for the prevention, identification, and management of occupational asthma
Bibliographic record
Abstract
Commentary on the paper by Nicholson et al (see page 290) The article by Nicholson et al in this issue of OEM has an ambitious aim: to assist the Health and Safety Executive in the reduction of occupational asthma by 30% in the next five years.1 The article is very well written and timely. Occupational asthma (OA) as noted by the authors is the most common chronic occupational lung disease now in most industrialised countries and most reported rates have not shown a decline in recent years. However, it is not clear as to what database will be used (both to establish baseline incidence and in the future) to determine changes in incidence of OA which may result from these guides—the SWORD scheme, compensation claims, or a new database? Is there knowledge which could be implemented to produce a decline of the magnitude stated? Preventive measures for OA designated by the authors as having a hypersensitivity cause, have been well described, and recently reviewed.2,3 Primary prevention can have dramatic effects when it is feasible. It can be achieved by preventing the exposures which lead to sensitisation and asthma. When an allergenic agent can be removed from a workplace, be substituted with a safe alternative, or be completely enclosed so that inhalation exposure does not occur, then rates of occupational asthma from that allergen in that setting should fall by 100%. This has been the observation when natural rubber latex (NRL) gloves are replaced in healthcare settings with non-NRL gloves, and also when enzymes in a workplace are encapsulated as part of a controlled programme. Even when exposure is markedly reduced, such as use of NRL gloves which have a low protein content and are powder-free, NRL occupational asthma rates fall to close to zero. Similarly estimates …
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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".