Cost‐Effectiveness of Various Diagnostic Approaches for Occupational Asthma
Bibliographic record
Abstract
BACKGROUND: Diagnosis of occupational asthma (OA) by specific inhalation challenge (SIC) can be costly and is not always available. The use of sputum testing to avoid this in some patients may be a more cost-effective alternative. OBJECTIVES: To compare the cost-effectiveness of SIC with serial measurements of sputum cell counts (sputum testing) and peak expiratory flow (PEF) monitoring. METHODS: Clinical data and testing costs for OA in 49 patients were collected during a previously published trial, modelled and compared using TreeAge Pro. Clinical outcome was the percentage of accurately diagnosed patients, using SIC as the gold standard. The PEF approach used the most accurate assessment of five experts who were blinded to SIC results. Differences in the proportion of eosinophils during periods on and off work were used for the sputum testing approach and in PEF/sputum for the combined approach. Unit costs were estimated from charges in Canadian hospitals. Data were analyzed by one-way and two-way analyses, and by probabilistic sensitivity analysis using a Monte Carlo simulation technique. RESULTS: The PEF approach had an estimated accuracy of 52% and cost $365 per patient tested. Compared with PEF monitoring, sputum testing was more accurate and cost an estimated $255 for each additional OA patient correctly diagnosed. SIC costs per additional correct diagnosis were $11,032 compared with sputum testing and $6,458 compared with PEF monitoring. The combined PEF/sputum testing approach was not cost-effective in the base case analysis, but cannot be excluded according to probabilistic sensitivity analyses. CONCLUSIONS: Although SIC remains the reference test to diagnose OA, when this test is not available, sputum testing is a cost-effective alternative to PEF for diagnosis of OA.
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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 | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".