A retrospective observational cohort study evaluating the postoperative outcomes of intracapsular coblation tonsillectomy in children
Bibliographic record
Abstract
Coblation intracapsular tonsillectomy (ICT) is becoming popular due to its decreased postoperative complications. However, a concern exists about the need for revision surgery. We conducted a retrospective observational cohort study, with a null hypothesis that Coblation ICT is not associated with recurrence of the preoperative symptoms, obstructive tonsillar regrowth, or the need for revision tonsillar surgery. We reviewed 345 patients (median age of 4.5 years; IQR 3.2-6.3), operated by the senior author between Feb 2017 and Sep 2020, for a median follow-up of 395.0 days (IQR 221.5-654.5). 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. The mean initial total symptoms score (TSS) was 5.2 (SD 1.4, range 1-8); 87.5% had three or more symptoms; 86.7% underwent ICT; TSS decreased postoperatively to a mean of 0.2, SD 0.8, range 0-7. The mean hospital stay was 0.96 day (SD 0.36, range 0-3). Secondary bleeding occurred in 0.7% of ICT patients. No patient required admission or intervention. There was no documented tonsillar regrowth resulting in upper airway obstruction. No one needed tonsillar revision surgery. Intracapsular tonsillectomy was shown to be an effective procedure with long-lasting results.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".