Prognostic model for accelerated decline in lung function due to occupational sensitizers: SAPALDIA study
Bibliographic record
Abstract
<b>Background:</b> Prognostic models can be used to screen individuals at increased risk of developing an outcome. <b>Aims and Objectives:</b> To develop models predicting the probability of developing accelerated lung function decline in subjects exposed to occupational sensitizers. <b>Methods:</b> The study population consisted of subjects of the SAPALDIA cohort who were exposed to occupational high-molecular-weight (n = 347) or low-molecular-weight agents at baseline (n = 246) with a mean follow up of 10.9 years. According to ATS, a mean decline > 60 mL/year in pre-bronchodilation FEV1 between baseline and follow-up was defined as “accelerated”. We used logistic regression analysis to develop the questionnaire-based model. We evaluated the gain of adding atopy and respiratory function test results to it. Discriminative ability, calibration and internal validity of the models were evaluated. <b>Results:</b> 99 of 593 (16.7%) subjects had an accelerated decline in FEV1. The questionnaire model (including age, sex, smoking status, body mass index, pre-existing asthma and type of sensitizing agents at baseline) showed a reasonable discrimination (corrected area under receiver operating characteristic curve (AUC) of 0.716 (95% CI 0.693–0.739)). Adding high FEV1 (> 80% of predicted value) at baseline significantly increased the discriminative ability (corrected AUC 0.756 95% CI 0.736–0.807). Both models showed good calibration and internal validity. <b>Conclusions:</b> The questionnaire and FEV1 model quantifies individual probability of accelerated lung function decline with good calibration and internal validity in workers exposed to occupational sensitizers. External validation of the model is necessary.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".