Middle turbinate suture technique: a cost-saving and effective method for middle meatal preservation after endoscopic sinus surgery.
Bibliographic record
Abstract
BACKGROUND: Lateralization of the middle turbinate following endoscopic sinus surgery (ESS) can lead to increased patient morbidity. Numerous techniques have been proposed to avoid this complication, including middle turbinectomy, stents, controlled synechiae formation, and metal clips. OBJECTIVES: To determine if a suture technique is an effective middle turbinate stabilization procedure and to determine the cost savings of this technique compared to commercially available middle meatal stents. MATERIAL AND METHODS: Retrospective review of 60 cases, all performed by the senior author using a middle turbinate suture technique, and the 3-month postoperative results. The efficacy of the technique was determined, as well as its cost compared to other materials for middle meatal preservation. RESULTS: A total of 110 turbinates were treated with the suture technique in 60 patients. The success rate was 98.2% (108 of 110). Commercial stent use cost ranged from 8 to 83 times the price of the suture depending on the stent. CONCLUSION: The middle turbinate suture technique is effective in preventing turbinate lateralization and has a significantly lower cost than commercially available middle meatal spacer materials.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".