Specific antibodies to diisocyanate and work-related respiratory symptoms in apprentice car-painters
Bibliographic record
Abstract
BACKGROUND: Isocyanates are the main cause of occupational asthma in most countries. Study of immunological markers of diisocyanate asthma may identify individuals at risk. OBJECTIVES: (1) To study changes in specific antibodies to hexamethylene diisocyanates (HDI); (2) to describe the incidence of work-related respiratory symptoms in relation to changes in specific antibody levels. METHODS: Prospective study in 385 apprentice car-painters during their 18 months of training. Participants were assessed on entering and completing their training using questionnaires, methacholine challenges and measurements of HDI-specific immunoglobulin E (IgE), immunoglobulin G (IgG) and subclass 4 of IgG (IgG4) antibodies. RESULTS: Complete data are available for 298 subjects. 13 subjects (4.4%) reported >or=1 new work-related lower respiratory symptoms and 19 (6.4%), >or=1 new work-related nasal symptoms. Increases in levels of specific IgE and IgG above the 97th and 95th percentiles were significantly associated with duration of exposure. Increase in specific IgG was inversely related to incidence of work-related lower respiratory symptoms (OR = 0.001, 95% CI 0.000 to 0.09) after adjusting for relevant covariates. The rise in specific IgG4 was significantly greater in those who did not develop work-related nasal symptoms (OR = 0.09, 95% CI 0.01 to 0.7). CONCLUSION: In this cohort of apprentice car-painters, a small proportion show increases in HDI-specific IgG and IgE after few months of exposure. Increases in specific IgG and IgG4 appear to have a protective effect on the incidence of work-related lower and upper respiratory symptoms, respectively. Assessment of specific antibodies to isocyanates may help identify subjects at risk of developing symptoms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".