Performance Accuracy of Wrist-Worn Oximetry and Its Automated Output Parameters for Screening Obstructive Sleep Apnea in Children
Bibliographic record
Abstract
Objectives: Obstructive sleep apnea (OSA) increases the risk of perioperative adverse events in children. While polysomnography (PSG) remains the reference standard for OSA diagnosis, oximetry is a valuable screening tool. The traditional practice is the manual analysis of desaturation clusters derived from a tabletop device using the McGill oximetry score. However, automated analysis of wearable oximetry data can be an alternative. This study investigated the accuracy of wrist-worn oximetry with automated analysis as a preoperative OSA screening tool. Methods: Healthy children scheduled for adenotonsillectomy underwent concurrent overnight PSG and wrist-worn oximetry. PSG determined the obstructive apnea-hypopnea index (OAHI). Oximetry data were auto-analyzed to determine 3% oxygen desaturation index (ODI3) and visually scored as per McGill criteria. The logistic regression model assessed the predictive performance of ODI3 for detecting the presence and severity of OSA after adjusting for covariates. Results: Seventy-six children (34 females), aged (mean±standard deviation) 5.7±1.6 years were classified, based on PSG-derived OAHI, as no OSA (n=31), mild (n=31), and moderate-severe OSA (n=14). Oximetric ODI3 was identified as the sole predictor of moderate-severe OSA (OAHI≥5 events/h) (odds ratio 1.38, 95% confidence interval 1.15, 1.65, p=0.001). The best diagnostic performance was at ODI3=5 events/h (78.6% sensitivity, 75.8% specificity [receiver operating characteristic-area under the curve {ROC-AUC}=0.857]). ODI3 was also more sensitive than the McGill oximetry score in diagnosing moderate-severe OSA (78.6% by ODI3 vs. 33.0% by McGill). The performance was suboptimal for any level of OSA (OAHI≥1 event/h) (75.6% sensitivity, 61.3% specificity [ROC-AUC=0.709]). Conclusions: Wrist-worn oximetry-derived automated ODI3 can reliably identify moderate-severe OSA in children undergoing adenotonsillectomy, making it a potentially useful preoperative OSA screening tool.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".