Diagnosing occupational asthma: insight from induced sputumThis paper is one of a selection of papers published in this Special Issue, entitled Young Investigator's Forum.
Bibliographic record
Abstract
The diagnosis of occupational asthma needs to be made objectively using as many criteria as possible. The latter include laboratory exposure tests with occupational agent(s), which are only available in specialized centers. Another approach is to monitor peak expiratory flow (PEF) and (or) methacholine airway responsiveness during periods at work and away from work. However, these measurements are open to misinterpretation when they are not optimally performed. Airway inflammation is one of the main characteristics of asthma. Induced sputum, a non-invasive method to assess airway inflammation, has been successfully used in the management of asthma. However, airway inflammation is seldom assessed during the investigation of occupational asthma. This article reviews the studies that have investigated and characterized the changes in sputum cell counts occurring in subjects with occupational asthma after exposure to occupational agents in the laboratory, or at the workplace to assess to place of induced sputum in the investigation 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.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".