A comparison of septal stapler to suture closure in septoplasty: a prospective, randomized trial evaluating the effect on operative time
Bibliographic record
Abstract
BACKGROUND: Septoplasty requires coaptation of the mucosal flaps at the conclusion of the procedure; classically this is done with nasal packing. Quilting sutures provide a welcome alternative to packing, but can be time-consuming to place. A septal stapler has recently been developed that provides a rapid alternative to quilting sutures but the timesaving has not been quantified. METHODS: This study was a prospective, randomized trial comparing a septal stapler to quilting suture for coaptation of mucosal flaps in septoplasty. After meeting inclusion criteria, patients underwent septoplasty and inferior turbinoplasty. The total operative time, surgical segment times, including time for closure was recorded. Preoperative and postoperative Nasal Obstruction Symptom Evaluation (NOSE) scores were recorded. A sample size of 16 was determined to detect a difference of 5 minutes in closure time. RESULTS: A total of 16 patients were enrolled in the study. The mean time for closure with septal stapler was 35 ± 22 seconds vs 7 minutes ± 1 minute 10 seconds for suture closure (p < 0.0001). The mean total operative time using the septal stapler was 28 minutes ± 6 minutes whereas 43 minutes ± 13 minutes was required for suture (p = 0.014). No difference in postoperative complications or mucosal healing was seen; preoperative and postoperative improvement in NOSE scores was comparable. CONCLUSION: Coaptation of the mucosal flaps in septoplasty with a septal stapler affords a timesaving in the operating room with no difference in operative outcome.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".