2023 CSACI annual scientific meeting book of abstracts
Bibliographic record
Abstract
Background: Occupational asthma accounts for nearly 18% of cases of adult-onset asthma, but prevalence rates vary between men and women.This may be due to sex distributions in industries and occupations associated with developing occupational asthma.The purpose of our study was to assess sex differences in occupational and clinical characteristics of patients with occupational asthma.Methods: We conducted a retrospective analysis of occupational and clinical data (including respiratory and allergy investigations) from patients with occupational asthma assessed at two tertiary care hospitals in Toronto, Canada, between 2000 to 2022.Our research questions were to elucidate sex differences in time to onset and diagnosis of occupational asthma and severity of occupational asthma, measured by spirometry and methacholine challenge testing.Results: Our study population included 255 patients with occupational asthma (159 males [62%] and 96 females [38%]).Males were more likely to work in the automotive industry (22% vs 4%, P < 0.05) and have had their employment terminated due to occupational symptoms (24% vs 3%, P < 0.05).Females predominated in healthcare (15% vs 1%, P < 0.001) and were more likely to have never smoked cigarettes (65% vs 47%, P < 0.05).Males tended to report a longer latency period between occupational exposure and development of asthmatic symptoms (36 months vs 18 months, P = 0.11) and a longer time to diagnosis (24 months vs 14 months, P = 0.08).In never-smokers, the mean FEV1/FVC ratio was significantly greater in females compared to males (75.90 vs 68.75, P < 0.001).There were no significant differences in PEF work variability, severity of airway hyperresponsiveness by methacholine challenge testing, and positivity to allergy skin tests.Conclusions: Though some limitations exist, our findings substantiate historical sex difference trends in occupational asthma distribution and exposures and suggest a longer time to diagnosis and increased disease severity in men compared to women.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.687 | 0.627 |
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".