A prognostic model of excessive lung function decline among Québec apprentices: a cohort exposed to occupational sensitizing agents
Bibliographic record
Abstract
BACKGROUND Forced expiratory volume in 1 second (FEV1) decline as a predictor of lung-related health problems is widely observed, but not fully investigated. This study aimed to develop models to predict FEV1 decline among apprentices exposed to sensitizing agents. METHODS Of 692 apprentices recruited and followed in 3.6–17.3 years, 292 were exposed to low-molecular weight agents. The analysis was restricted to 357 apprentices with complete lung function assessment at the end of their training with a minimum of 5-year follow-up. According to the American Thoracic Society guideline, a mean FEV₁ decline >60 ml/year was defined as “accelerated.” Descriptive statistics and Cox regression analysis were utilized to determine its predictors. To develop the prognostic models, we used a logistic regression analysis adjusted for the follow-up duration. The accuracy of the models was quantified using calibration and discrimination measures. RESULTS Of 357 subjects, 62 (17.4%) had an excessive FEV1 decline post-apprenticeship. The questionnaire model (model 1), which included male sex, wheezing, and exposure to isocyanate or animal allergens during the apprenticeship, showed a reasonable discriminative ability (area under the receiver operating characteristics curve [AUC] of 0.67, 95% CI = 0.59–0.75). Adding the percent-predicted FEV₁ value at the end of apprenticeship significantly increased the discriminative ability of the model (model 4) (AUC = 0.762, 95% CI = 0.694–0.829) with a good calibration and reasonable internal validity. CONCLUSIONS We developed a model for accelerated lung function decline with a good accuracy and internal validity. However, external validation of the model is necessary.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".