Use of intranasal submucosal fillers as a transient implant to alter upper airway aerodynamics: implications for the assessment of empty nose syndrome
Bibliographic record
Abstract
BACKGROUND: Empty nose syndrome (ENS) is a debilitating condition associated with inferior turbinate tissue loss. Surgical augmentation of the inferior meatus has been proposed to treat ENS, although efficacy data with validated, disease-specific questionnaires is limited. Instead we evaluated submucosal injection of a transient, resorbable filler into the inferior meatus to favorably alter nasal aerodynamics in ENS patients. METHODS: Patients with a history of inferior turbinate reduction, diagnosed with ENS via Empty Nose Syndrome 6-Item Questionnaire (ENS6Q) and cotton testing, were enrolled and underwent submucosal injection of carboxymethylcellulose/glycerin gel (Prolaryn®) into the inferior meatuses between July 2014 and May 2018. This material likely resorbs over several months. Outcomes included comparisons of preinjection and postinjection symptoms at 1 week, 1 month, and 3 months using the ENS6Q, 22-item Sino-Nasal Outcome Test (SNOT-22), Generalized Anxiety Disorder 7-item scale (GAD-7), and Patient Health Questionnaire-9 (PHQ-9). RESULTS: Fourteen patients underwent injections. Mean ENS6Q scores significantly decreased from baseline at 1 week (20.8 vs 10.5; p < 0.0001), and remained reduced but upward-trending at 1 month (13.7, p = 0.002) and 3 months (15.5, p > 0.05) following injections. Mean SNOT-22 scores significantly decreased at 1 week (p = 0.01) and 1 month (p = 0.04), mean GAD-7 at 1 month (p = 0.02) and 3 months (p = 0.02), and mean PHQ-9 at 1 week (p = 0.01) and 1 month (p = 0.004) postinjection. CONCLUSION: Transient, focal airway bulking via submucosal filler injection at sites of inferior turbinate tissue loss markedly benefits ENS patients, suggesting that aberrant nasal aerodynamics from inferior turbinate tissue loss contributes to (potentially reversible) ENS symptoms.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".