Worldwide trends in incidence in occupational allergy and asthma
Bibliographic record
Abstract
PURPOSE OF REVIEW: Recent improvements in the methods for analyzing trends in occupational health surveillance and the prospect of future improvements in the collecting and sharing of electronic data alongside increasing availability of linked datasets make this a good time to review the existing literature on trends in occupational allergy and asthma (OAA). RECENT FINDINGS: There is a notable lack of reports of recent trends in OAA in the academic literature and much of the published work comes from European countries. The incidence of OAA appears to be declining based on physician-reporting or recognized compensation claims for the countries with published data. However, we need to be cautious in interpreting this as a decline in the 'true' incidence of OAA. Few of the studies adjusted appropriately for changes in the population at risk and one of the most robust study designs showed no change in the incidence of allergic contact dermatitis in contrast to the other studies. SUMMARY: Many existing datasets have the potential to be used to examine trends, and studies from Canada show the potential of using linked databases for surveillance. We hope that this review will encourage improvements in the analysis, and more dissemination, of trends.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".