Utilization of Predictive Models for Diagnosis of Occupational Diseases
Bibliographic record
Abstract
Predictive models have long been used to assist clinical decision-making in medicine. Predictive models are made to estimate how likely a person is to have a disease (diagnostic model) or will experience a disease (prognostic model). In the field of occupational health, for example, diagnostic models can be used to increase the efficiency of surveillance programs by identifying groups of workers with occupational diseases without using complex and expensive diagnostic tests.Work-related asthma (WRA) is the most common occupational lung disease in industrialized countries and the second most common in developing countries. Around the world, especially in developing countries, diagnosing WRA is still difficult due to the limitations of available diagnostic tests. Specific inhalation challenge (SIC), the best test for diagnosing occupational asthma, is only available in several research centres worldwide.Several questionnaire-based models have been developed to diagnose work-related asthma at both the primary (general practitioner) and secondary (specialist) levels of care. A recent model for diagnosing occupational asthma was developed using data from Canada and has been validated using data from several European countries. A collaboration has been initiated to assess the application of this model among Indonesian workers.
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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 | 0.001 | 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".