Correlation between acoustic rhinometry and subjective nasal patency during nasal challenge test in subjects with suspected occupational rhinitis; a prospective controlled study
Bibliographic record
Abstract
OBJECTIVES: To assess the correlation between acoustic rhinometry and visual analogue scale endpoints in the context of nasal challenge with occupational agents. DESIGN: Prospective controlled study. SETTING: University teaching hospital. PARTICIPANTS: Sixty-seven subjects with a history of work-related rhinitis and asthma symptoms. MAIN OUTCOMES MEASURES: Subjects underwent nasal challenge with control and specific agent on consecutive days. Nasal congestive response to challenge was monitored by acoustic rhinometry and visual analogue scale. RESULTS: Results showed no correlation between visual analogue scale and acoustic rhinometry measurements at baseline on the control (r=-0.13, P=0.3) and active (r=0.14, P=0.2) challenge days. No correlation was found between acoustic rhinometry and visual analogue scale when analysing all measurements obtained at all times after challenge with the control and active agent (control: r=0.09, P=0.04; active: r=0.001, P=0.9). The correlation between acoustic rhinometry and visual analogue scale was good and significant (r=-0.62, P=<0.01) when the analysis was restricted to cases showing a decrease in nasal volume>40% from baseline values. CONCLUSIONS: We showed that the correlation between acoustic rhinometry and subjective nasal patency was poor on steady conditions. However, a significant correlation was observed in those cases showing a greater nasal congestive response after challenge measured by acoustic rhinometry.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".