Intensive Care Unit Monitoring Post-Tonsillectomy in Children with Obstructive Sleep Apnea
Bibliographic record
Abstract
Objective There is a lack of consensus regarding postoperative care for pediatric patients with obstructive sleep apnea (OSA) following adenotonsillectomy. At our institution, all patients with severe OSA are routinely admitted to the pediatric intensive care unit (PICU), raising concerns about the optimal use of health care resources. The objective of this study was to identify the risk factors necessitating PICU admission for pediatric patients who underwent adenotonsillectomy for OSA. Methods An 8 year retrospective cohort study was conducted at a tertiary care pediatric hospital among consecutive patients with confirmed OSA undergoing adenotonsillectomy. All patients for whom a preoperative PICU request was made were included. A patient requiring PICU-level care was defined as needing respiratory support, such as intubation, positive pressure ventilation, or high-flow nasal cannula. Results A total of 112 medical charts were included in the analysis. Only 13 patients (11.6%) had respiratory complications requiring PICU-level care. No preoperative or intraoperative variables were predictive of need for PICU. Early-postoperative need for supplemental oxygenation ( P = .002, OR = 6.7) and respiratory retraction ( P < .000, OR = 27.4) were significant predictors of PICU-level airway escalation. Nearly all patients (11/13) requiring escalated airway measures were identified in the first 4 hours postoperatively. Conclusion A small subset of subjects with OSA required PICU-level care after adenotonsillectomy. Our data suggest that pediatric patients with OSA undergoing adenotonsillectomy may be safely monitored outside of an ICU setting for an extended period before determining eventual care setting.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| 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".