An Effective Strategy for Diagnosing Occupational Asthma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| 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 it