Reproducibility of Acoustic Rhinometry in the Investigation of Occupational Rhinitis
Bibliographic record
Abstract
BACKGROUND: To diagnose occupational rhinitis, it is mandatory to conduct an objective assessment of changes in nasal patency during specific inhalation challenge (SIC). The reproducibility of acoustic rhinometry measurements in the setting of occupational challenges has never been examined. This study assessed the reproducibility of acoustic rhinometry during SIC investigation of occupational rhinitis. METHODS: Twenty-four subjects underwent acoustic rhinometry measurements during SIC investigation of occupational rhinitis. Subjects attended 3-6 days of SIC within a week by means of a realistic or closed-circuit apparatus methodology RESULTS: All of the within-day intraclass correlation coefficients (ICCs) for nasal volume (2-5 cm) and minimum cross-sectional area (MCA) based on a different number of measurements (2-7) were above 0.85; all of the coefficients of variation (CVs)for the same parameters were low (below 10%). The between-day CVs based on different numbers of SIC sessions ranged from 8.0 to 8.8% and from 6.8 to 8.8%for nasal volume and MCA, respectively. The between-day ICCs ranged from 0.80 to 0.88 and from 0.83 to 0.94 for nasal volume and MCA, respectively. CONCLUSION: Acoustic rhinometry showed good within- and between-day reproducibility and can be recommended for the objective monitoring of nasal patency during SIC investigating occupational rhinitis.
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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 | 0.026 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".