Overnight oximetry in children undergoing adenotonsillectomy: A single center experience
Bibliographic record
Abstract
BACKGROUND: Obstructive sleep apnea (OSA) is the most common indication for adenotonsillectomy in children. Home-based sleep oximetry continues to be used in the diagnosis of pediatric OSA despite a lack of correlation with lab-based polysomnography. This study investigates whether factors influence surgeons in selecting patients for home-based sleep oximetry, how the study findings are used in patient management, and whether abnormal oximetry findings are associated with post-operative complications. METHODS: A retrospective review was performed on children with suspected OSA who had undergone a tonsillectomy and/or an adenoidectomy over a three-year period. Demographic features, comorbidities, pre-operative oximetry results, and post-operative complications were recorded. Data analysis consisting primarily of logistic regression was performed using Stata 12.0 (College Station, Texas). RESULTS: Data was collected from 389 children. Two hundred and seventy-one children underwent pre-operative oximetry (69.7%). There was no significant association between age or the presence of comorbidities and the likelihood of undergoing pre-operative sleep oximetry. The post-operative complication rate was 0.8%. There was no significant association between abnormal sleep oximetry parameters and post-operative complications. Children with one or more abnormal sleep oximetry parameters were more likely to be observed in hospital for at least one night (OR 2.4, p < 0.0001). CONCLUSIONS: Our study suggests that surgeons are using home-based sleep oximetry findings to inform the post-operative care of children with suspected OSA, as those with abnormal home-based sleep oximetry findings were more likely to be observed in hospital. These hospital admissions may be unnecessary given the poor correlation of home-based oximetry and PSG as well as the low rate of serious post-operative complications.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".