The Effectiveness of Modified Cottle Maneuver in Predicting Outcomes in Functional Rhinoplasty
Bibliographic record
Abstract
Objective. To assess the outcomes of functional rhinoplasty for nasal valve incompetence and to evaluate an in-office test used to select appropriate surgical techniques. Methods. Patients with nasal obstruction due to nasal valve incompetence were enrolled. The modified Cottle maneuver was used to assess the internal and external nasal valves to help select the appropriate surgical method. The rhinoplasty outcomes evaluation (ROE) form and a 10-point visual analog scale (VAS) of nasal breathing were used to compare preoperative and postoperative symptoms. Results. Forty-nine patients underwent functional rhinoplasty evaluation. Of those, 35 isolated batten or spreader grafts were inserted without additional procedures. Overall mean ROE score increased significantly (P < 0.0001) from 41.9 ± 2.4 to 81.7 ± 2.5 after surgery. Subjective improvement in nasal breathing was also observed with the VAS (mean improvement of 4.5 (95% CI 3.8-5.2) from baseline (P = 0.000)). Spearman rank correlation between predicted outcomes using the modified Cottle maneuver and postoperative outcomes was strong for the internal nasal valve (Rho = 0.80; P = 0.0029) and moderate for the external nasal valve (Rho = 0.50; P = 0.013). Conclusion. Functional rhinoplasty improved subjective nasal airflow in our population. The modified Cottle maneuver was effective in predicting positive surgical outcomes.
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".