How Allergists Can Perform an Occupational History in Every Patient
Bibliographic record
Abstract
The occupational history is often neglected in the routine evaluation of new patients with asthma, chronic rhinitis, or dermatologic complaints. Such omissions are inadvertent because work-related conditions are often not prioritized. There also may be lack of awareness of the scope of respiratory or cutaneous allergens capable of inducing occupational asthma (OA) or work-related contact dermatitis. Evidence exists suggesting that the occupational history is often neglected among primary care physicians and specialists. Failure to diagnose OA in a timely fashion by identifying occupational sources of exposure, for example, may result in unnecessary morbidity in workers whose exposure is not modified. In this commentary, we propose a brief intake survey to be administered to all patients coming to an allergy practice to quickly screen for possible work-related respiratory symptoms and another for occupational dermatitis. This would require minimal physician time and could be self-administered at the initial encounter and incorporated into the medical record. A positive response to either survey should trigger a more detailed evaluation by the allergy specialist. More detailed approaches for stepwise clinical evaluation of the worker suspected of OA and contact dermatitis are discussed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".