An Effective Strategy for Diagnosing Occupational Asthma
Bibliographic record
Abstract
Monitoring airway inflammation by means of induced sputum cell counts seems to improve the management of asthma. We sought to assess whether such monitoring at the end of periods at and away from work combined with the monitoring of PEF could improve the diagnosis of occupational asthma. We enrolled subjects suspected of having occupational asthma. Serial monitoring of PEF was performed during 2 weeks at and away from work. At the end of each period, induced sputum was collected. Specific inhalation challenge was subsequently performed. PEF graphs were interpreted visually by five independent observers. Forty-nine subjects, including 23 with positive specific inhalation challenge, completed the study. The addition of sputum cell counts to the monitoring of PEF increased the specificity of this test, respectively, by 18 (range [r] 13.7-25.5) or 26.8% (r 24.8-30.4) depending if an increase of sputum eosinophils greater than 1 or 2% when at work was considered as significant. The sensitivity increased by 8.2% (r 4.1-13.4) or decreased by 12.3% (r 3.1-24.1) depending on the cutoff value in sputum eosinophils chosen (greater than 1 or 2%, respectively). The addition of sputum cell counts to PEF monitoring is useful to improve the diagnosis of occupational asthma.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".