The clinical value of peak nasal inspiratory flow, peak oral inspiratory flow, and the nasal patency index
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: The aim of this study was to ascertain the most reliable objective measurement for the assessment of nasal patency by investigating the relationship between peak nasal inspiratory flow, peak oral inspiratory flow, and the nasal patency index in relation to the patient's subjective perception regarding nasal obstruction. STUDY DESIGN: Prospective cohort study. METHODS: This study included 131 volunteers of both genders, aged 18 years or older, with or without nasal symptoms, who were able to give informed consent, completed the study protocol, and could speak and write Dutch fluently. Peak nasal inspiratory flow and peak oral inspiratory flow were performed and nasal patency index was computed. The results were evaluated and compared with the subjective perception of nasal passage, using the validated Nasal Obstruction Symptom Evaluation scale and visual analog scale for nasal passage. RESULTS: Our study showed that peak nasal inspiratory flow, nasal patency index and nasal patency visual analog scale correlate with the Nasal Obstruction Symptom Evaluation scale in contrast to peak oral inspiratory flow. Peak nasal inspiratory flow and nasal patency index also showed significant association with the Nasal Obstruction Symptom Evaluation scale after adjustment for confounders. CONCLUSIONS: Peak nasal inspiratory flow is the most reliable method for the assessment of nasal patency. It is quick, inexpensive, and easy to perform, and correlates significantly with the subjective feeling of nasal obstruction. There is no clinical need to measure peak oral inspiratory flow or to calculate the nasal patency index in the evaluation of nasal patency. LEVEL OF EVIDENCE: 4
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".