Preliminary Estimates of the Diagnostic Accuracy of Video Clips for Obstructive Sleep Apnea in Children
Bibliographic record
Abstract
BACKGROUND: Diagnosing obstructive sleep apnea (OSA) in children is challenging, with long wait times for polysomnography (PSG). This study assessed the diagnostic accuracy of home-recorded video clips for OSA compared to PSG. METHODS: Children (2-18 years) referred for PSG for suspected OSA were enrolled. Parents recorded video clips of their child sleeping and completed the Pediatric Sleep Questionnaire (PSQ). Blinded clinicians scored videos using the Monash Obstructive Sleep Apnea score (MS). Participants underwent PSG, and outcomes included obstructive apnea-hypopnea index (OAHI) and oximetry metrics (i.e., McGill Oximetry Score [MOS]; 3% Oxygen Desaturation Index [ODI3]). Diagnostic characteristics of MS, PSQ, MOS, and ODI3 were compared for detection of any (OAHI ≥ 1.5 events/h) and moderate-severe OSA (OAHI ≥ 5 events/h). RESULTS: Forty-one children (age 7.0 years, 49% female) participated. Median OAHI was 0.6 events/h (IQR 0.3, 3.1); 16 (39%) had OAHI ≥ 1.5 events/h, 5 (12%) had OAHI ≥ 5 events/h. PSQ identified 36 (88%) participants with a score ≥ 0.33. One child had MOS ≥ 2; ODI3 was ≥ 4.3 in 8 (20%) and > 7 in 6 (15%). Mean MS was 3.6 (SD 2.1). MS had 81.2% sensitivity and 52.0% specificity for any OSA and 100% sensitivity and 44.4% specificity for moderate-severe OSA. A combination of MS and ODI3 improved diagnostic accuracy with an AUC of 98.3. CONCLUSION: MS demonstrated high sensitivity but low specificity for the detection of moderate-severe OSA. Video scores outperformed PSQ but were less accurate than oximetry. Combining MS and ODI3 yielded the strongest diagnostic characteristics. Video scores may aid in pediatric OSA screening.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".