Which tools best predict the incidence of work-related sensitisation and symptoms
Bibliographic record
Abstract
BACKGROUND/AIM: This study used information from the questionnaire alone or in conjunction with clinical tests, such as skin-prick testing (SPT) and bronchial responsiveness (BR) testing at entry, to develop models for estimating the probability of the occurrence of specific IgE-sensitisation to and respiratory symptoms in contact with laboratory animal (LA) allergens after 32 months' training in an animal health technology programme. METHODS: Four multivariable logistic regression models were developed for each endpoint, consisting of: (1) questionnaire; (2) questionnaire and SPT; (3) questionnaire and BR testing; and (4) questionnaire, SPT and BR testing. The prognostic models were derived from a cohort of Canadian animal health technology apprentices. The models' internal validity and diagnostic accuracy were evaluated and compared. RESULTS: Symptoms indicative of asthma and allergic symptoms at baseline composed the final questionnaire model for the occurrence of occupational sensitisation and symptoms. Both questionnaire models showed a good discrimination (area under the receiver operating characteristics curve were 0.73 and 0.78, respectively) and calibration (Hosmer-Lemeshow test p value >0.10). Addition of SPT and/or BR testing increased the specificity of the questionnaire model for LA sensitisation, but not for symptoms at work. To facilitate their application in practice, the final questionnaire models were converted to easy-to-use scoring system. CONCLUSIONS: Questionnaire is an easy tool that can give accurate prediction of the incidence of occupational sensitisation and 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 | 0.005 | 0.022 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".