New methodology for specific inhalation challenges with occupational agents
Bibliographic record
Abstract
BACKGROUND: Inhalation challenges are used for diagnosing occupational asthma (OA). The initial methodology consisted of a "realistic" exposure without monitoring nor controlling exposure. Our aim was to design an equipment, called the GenaSIC, that allows the generation of various agents regardless of the formulation and to assess the feasibility of its use in patients investigated for OA. RESULTS: GenaSIC can generate lactose, flour, malt, isocyanates, formaldehyde and N-butyl acetate with precise and fairly stable concentrations. Using N-butyl-acetate as a control agent and real time measurement, we show that normal breathing has a negligible effect on the concentration. We exposed forty-four different subjects to a control agent and/or to a suspected occupational agent. Nineteen of the subjects were only exposed to N-butyl acetate as a control agent without experiencing any significant irritant effect (no significant changes in spirometry thereafter). Eight subjects who were exposed to both N-butyl acetate and formaldehyde did not show significant reactions. Seven subjects were exposed to dry particles (flour in six instances, malt in the other) and five showed immediate asthmatic reactions which changes in FEV1 from 20% to a maximum of 28%. Finally, ten subjects were exposed to isocyanates, four of whom showed a positive reaction, including one subject with immediate maximum changes in FEV1 of 22%. CONCLUSION: GenaSIC offers the possibility of reliable and safe exposures to dry particles, formaldehyde and isocyanates in the investigation 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".