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Record W4396952016 · doi:10.1016/j.jaip.2024.05.012

How Allergists Can Perform an Occupational History in Every Patient

2024· review· en· W4396952016 on OpenAlexaff
David I. Bernstein, Karin Pacheco, Catherine Lemière

Bibliographic record

VenueThe Journal of Allergy and Clinical Immunology In Practice · 2024
Typereview
Languageen
FieldMedicine
TopicOccupational exposure and asthma
Canadian institutionsUniversité de Montréal
FundersAmerican Academy of Allergy Asthma and Immunology
KeywordsMedicineOccupational asthmaAsthmaMedical historyAllergyOccupational medicineOccupational exposureMedical recordFamily medicineIntensive care medicineMedical emergencySurgeryImmunology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.082
GPT teacher head0.410
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations4
Published2024
Admission routes1
Has abstractyes

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