Reproducibility of Nasal Lavage in the Context of the Inhalation Challenge Investigation of Occupational Rhinitis
Bibliographic record
Abstract
BACKGROUND: The nasal lavage (NAL) method is increasingly used to assess changes in upper airways inflammation in the investigation of occupational rhinitis (OR). A good reproducibility of the method is fundamental to accurately assess changes in markers of inflammation in nasal secretions before and after inhalation challenges. The main objective of this study was to assess the short-term reproducibility of cells and cellular markers of inflammation in NAL in the setting of specific inhalation challenge (SIC) investigating OR. An ancillary objective was to assess the reproducibility of NAL in the context of two different SIC methodologies. METHODS: Twenty-five subjects attended the laboratory for 2 separate days of NAL performed within the same week. On the first visit subjects underwent NAL before a SIC sham session and on the second visit before a SIC with the active agent. These prechallenge NAL measurements obtained on both days were used to analyze the reproducibility of the NAL method. RESULTS: The reproducibility for cell differential counts was satisfactory for neutrophils (intraclass correlation coefficient [ICC] = 0.68), for eosinophils (ICC = 0.95), for macrophages (ICC = 0.77), and for epithelial cells (ICC = 0.73). The reproducibility of total cell counting was poor (ICC = 0.12). The reproducibility of ECP concentrations was satisfactory (ICC = 0.67). Eosinophil counts were reproducible in the context of two different challenge methodologies. CONCLUSION: The NAL method was shown to be sufficiently reproducible to be considered useful for the monitoring of upper airways inflammation during the investigation of OR by SIC.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".