Investigation of Occupational Asthma: Do Clinicians Fail to Identify rRelevant Occupational Exposures?
Bibliographic record
Abstract
BACKGROUND: Specific inhalation challenges (SIC) enable the identification of the agent responsible of occupational asthma (OA). A clinician may fail to identify a specific agent in the workplace, which may potentially lead to a misdiagnosis. The expert assessment method performed by an occupational hygienist has been used to evaluate occupational exposures in epidemiological studies. OBJECTIVE: The broad aim of the present study was to evaluate the contribution of an expert assessment performed by an occupational hygienist to the diagnosis of OA. The specific aim was to compare workplace exposures identified by an occupational hygienist and by chest physicians in subjects with positive SICs and subjects with asthma, but with a negative SIC. METHODS: SICs were performed in 120 cases: 67 were positive and 53 were negative. A clinician assessed occupational exposures to sensitizers during a routine clinical evaluation preceding the performance of the SIC. An expert assessment of occupational exposures was performed by an occupational hygienist blind to the result of the SIC. RESULTS: The occupational hygienist identified the causal agent in 96.7% of the 61 cases of positive SIC. In 33 (62.3%) cases of negative SICs, the occupational hygienist identified ≥1 sensitizing agent(s) that had not been identified by the clinician. CONCLUSION: The hygienist identified the causal agent in almost all subjects with OA. In contrast, the clinician failed to identify potential exposures to sensitizers in >60% of the negative SIC subjects, which may have resulted in some subjects being misdiagnosed as not having OA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".