An Official American Thoracic Society Workshop Report: Presentations and Discussion of the Sixth Jack Pepys Workshop on Asthma in the Workplace
Bibliographic record
Abstract
The Sixth Jack Pepys Workshop on Asthma in the Workplace focused on six key themes regarding the recognition and assessment of work-related asthma and airway diseases: (1) cleaning agents and disinfectants (including in swimming pools) as irritants and sensitizers: how to evaluate types of bronchial reactions and reduce risks; (2) population-based studies of occupational obstructive diseases: use of databanks, advantages and pitfalls, what strategies to deal with biases and confounding?; (3) damp environments, dilapidated buildings, recycling processes, and molds, an increasing problem: mechanisms, how to assess causality and diagnosis; (4) diagnosis of occupational asthma and rhinitis: how useful are recombinant allergens (component-resolved diagnosis), metabolomics, and other new tests?; (5) how does exposure to gas, dust, and fumes enhance sensitization and asthma?; and (6) how to determine probability of occupational causality in chronic obstructive pulmonary disease: epidemiological and clinical, confirmation, and compensation aspects. A summary of the presentations and discussion is provided in this proceedings document. Increased knowledge has been gained in each topic over the past few years, but there remain aspects of controversy and uncertainty requiring further research.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.042 | 0.016 |
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".