The cotton test redistributes nasal airflow in patients with empty nose syndrome
Bibliographic record
Abstract
BACKGROUND: Empty nose syndrome (ENS) remains a controversial disease primarily associated with inferior turbinate tissue loss. Cotton placement into the inferior meatus often alleviates ENS symptoms within minutes, but the physiologic explanation for this phenomenon is unknown. Computational fluid dynamics (CFD) was employed to evaluate the mechanisms of altered nasal airflow conferred by cotton testing. METHODS: Six ENS patients (12 sides) with pre-existing sinus computed tomography (CT) imaging were enrolled after marked symptomatic improvement (decrease in score on the Empty Nose Syndrome 6-Item Questionnaire [ENS6Q] of >7 points) with office-based cotton testing. The fashioned cotton plug was labeled in situ with iohexol contrast spray, and sinus CT was immediately obtained to detect cotton contouring in the inferior meatus. CT imaging from pre- and post-cotton placement was analyzed using comparative CFD techniques. RESULTS: After cotton placement, significant symptomatic improvement and reduced ENS6Q scores (16.8 ± 4.1 to 3.1 ± 2.4; p < 0.001) were recorded. Using CFD, cotton placement produced an expected 21% increase in upper airway resistance (p < 0.05). However, a significant shift in the nasal airflow distribution was also detected, with a transition of airflow vectors away from a middle meatus jetstream (-41%; p < 0.002). CONCLUSION: Objective CFD assessment confirmed that the cotton test not only increases nasal resistance, but also restores airflow distribution to the inferior meatus in symptomatic ENS patients. These results highlight the potential efficacy of cotton test in ENS patients and further bolster the utility of this tool in identifying appropriate candidates for the inferior meatus augmentation procedure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".