Ninety-Day Emergency Department Rebound Following Adult Tonsillectomy: A Retrospective Cohort Study
Bibliographic record
Abstract
ImportancePost-tonsillectomy complications often present in emergency departments (EDs). Reducing postoperative ED visits is one strategy to relieve the strain on healthcare systems and patients.ObjectiveTo assess the rate and reason for ED visits within 90-days post-discharge from adult tonsillectomy.DesignRetrospective cohort study.SettingNova Scotia, Canada.ParticipantsAll adult patients (≥16 years old) with a Nova Scotia Healthcare card who underwent a tonsillectomy in Nova Scotia, Central Zone from April 1, 2016 to March 31, 2022, and had an ED visit anywhere in Nova Scotia from April 1, 2016 to June 30, 2022, to allow a 90-days post-discharge window.MethodsRetrospective chart review utilizing administrative datasets for province-wide ED visits within 90-days post-discharge from an adult tonsillectomy. The patients' first ED visit postoperation was analyzed.ResultsOverall, 356 adult patients underwent tonsillectomy, of which 129 (36.2%) presented to the ED within 90 days. Of these, 99 were related to the tonsillectomy, resulting in a surgery-specific ED rebound rate of 27.8%. Most surgical ED visits (84/99, 84.8%) occurred within 7 days, most commonly for bleeding (47/99, 47.5%) and pain (36/99, 36.4%). Of the surgical visits, 26/99 (26.3%) were admitted, with 22/26 (84.6%) for bleeding. Of the surgical visits not related to bleeding, 48/52 (92.3%) were discharged home or left without being seen, which suggests 48/99 (48.5%) surgical ED visits may be preventable.ConclusionThe ED rebound rate for visits related to the tonsillectomy was 27.8% in our population. Given the potentially severe consequences of post-tonsillectomy bleeding, a high ED visit rate may be necessary. However, optimization of postoperative pain control along with greater access to urgent outpatient otolaryngology and primary care resources may reduce the burden of ED visits. This data adds to recent literature suggesting a higher rate of healthcare usage post-adult tonsillectomy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".