How do we safely increase day-case tonsillectomy for the treatment of paediatric obstructive sleep apnoea -- a cohort analysis
Bibliographic record
Abstract
Background: There is an increasing importance to increasing the day-case rate for children undergoing adenotonsillectomy. The primary aim of this study was to evaluate the immediate post-operative complication (IPOC) rate of children undergoing adenotonsillectomy for the treatment of paediatric obstructive sleep apnoea (OSA), with a view to increasing the day-case rate. IPOC was defined as any adverse clinical events experienced if admitted, or as a re-presentation to the emergency department/ward if done as a day-case, within 24 hours of the surgery. The secondary aim was to evaluate the risk factors predictive of IPOC. Methods: A retrospective analysis of children undergoing adenotonsillectomy for OSA between 01/11/2019–31/03/2022. Results: 464 children were included. Children done as a day-case experienced 0% IPOC (n=260; 220 were planned day-case). Children done as an inpatient experienced 16.7% IPOC (n=34/204). Every child who experienced IPOC had one or more of the following four clinical features: age <3 years, <15 kg, >98th weight centile, significant medical comorbidities. 269 children had none of these four clinical features, and experienced 0.371% IPOC (n=1/269; primary post-tonsillectomy bleed). Children with pre-operative oximetry scores of McGill 3-4 experienced 0% IPOC if they had none of the four clinical features (n=20). The overall readmission rate was 2.80% (n=13/464). Conclusion: Our experience suggests children with none of the four clinical risk factors identified can have adenotonsillectomy performed as a day-case procedure, irrespective of the pre-operative oximetry results. Pre-operative oximetry does not appear to add any additional value in predicting adverse post-operative events.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".