Influence of Sinus Surgery in Rhinometric Measurements
Bibliographic record
Abstract
The objectives of this prospective study were to clarify how well acoustic rhinometry (AR), computed tomography volumetry (CTV), rhinomanometry (RMM), and the visual analogue scale (VAS) identify changes in intranasal pathology after endoscopic sinus surgery in patients with chronic sinusitis. The measurements were performed in 44 nasal cavities of 11 patients pre- and postoperatively, 6 of whom underwent middle meatal antrostomy and 5 of whom underwent ethmoidectomy. The AR and RMM results were compared with those obtained with CTV and VAS. Furthermore, a favourable outcome in sinus surgery was obtained with all of the methods. The results showed clearly that endoscopic sinus surgery significantly changes the intranasal geometry and can be obtained reliably using rhinometric measurements. Both AR and CTV identified statistically significant (p < .05) volume changes in the nasal cavities. The AR and CTV results correlated generally well (r = .72) with each other, but wide differences were seen between the operative groups. Correlation in the ethmoidectomy group was very strong (r = .93) but weak in the middle meatal antrostomy group (r = .37). In the nasal function measurements, nasal obstruction decreased significantly (p < .05) after the surgery. The changes were clearly obtained using RMM and VAS (p < .05). Correlation between these methods was generally poor (r < .30), but a difference was again seen in the operative groups. In the ethmoidectomy group, correlation was moderately good (r = .55) but weak in the middle meatal antrostomy group (r = .29). We concluded that rhinometric methods are reliable tools for evaluation of operative outcome in endoscopic sinus surgery patients. Inspiratory resistance measured with RMM and nasal obstruction assessed with VAS appeared to measure separate parameters in nasal function.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".