Coblation Adenotonsillar surgery: a game changer in decreasing postoperative complications while maintaining efficacy – an audit of 345 cases
Bibliographic record
Abstract
Abstract Purpose: To assess the safety and efficacy of coblation adenotonsillar surgery with emphasis on intra-capsular tonsillectomy (ICT) in children with both obstructive and infective indications.Methods: Children, 6 months to 18y of age, who underwent coblation adenotonsillar surgery by the senior author between Feb 2017 and Sep 2020, were included. We reviewed the demographic data, preoperative and postoperative symptoms and degree of obstruction, postoperative complications, the need for revision surgery.Results: We reviewed 345 patients; median age 4.5y (11 months – 16.3y, mean 5.2y). Most patients had snoring (94.2%), mouth breathing (92.8%), restless sleep (62.6%), and sleep disorder breathing (52.8%), 12.5% had recurrent tonsillitis. Median initial total symptoms score (TSS) was 4.5 (1-8; mean 4.1); 87.5% having 3 or more symptoms; this decreased postoperatively to a median of 0.0 (range 0 – 7; mean 0.2). Most patients underwent ICT (86.7%). Postoperative course was uneventful in most patients with median hospital stay of 1 day (0-3, mean1). Secondary bleeding was 1.7% [66.7% in extracapsular tonsillectomy (ECT)], none required admission or intervention. There was no tonsillar regrowth resulting in upper airway obstructive symptoms. None needed tonsillar revision surgery (only one patient needed revision adenoidectomy), at a median follow up of 27 months (range 9-47, mean 26.6). Conclusions: Coblation adenotonsillar surgery is a safe and effective procedure; ICT reduced the risk for postoperative bleeding to 0.7% and was self-limiting. The need for another tonsillar surgery was nil. We adopted ICT for both obstructive and infective indications based on our results and those of other published data.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".