Screening for Obstructive Sleep Apnea in the Resource-limited Setting of the COVID-19 Pandemic (SPARE Study)
Bibliographic record
Abstract
Abstract Introduction: Obstructive sleep apnea (OSA) significantly impacts children's health, but diagnosing OSA is challenging, which was further impacted during the COVID-19 pandemic when access to polysomnography (PSG) was even more restricted. A recent study used videos of children during sleep to assess OSA and developed a scoring system (Monash score). It demonstrated 100% sensitivity and 36% specificity for moderate-to-severe OSA diagnosis when video score was ≥3. Hypothesis: We hypothesized that home video recordings with mobile technologies could offer a practical tool for clinicians to identify children with moderate-to-severe OSA. Methods: This study included children aged 3-18 years, referred for OSA evaluation. Parents recorded a three-minute video of their children sleeping on three separate nights, focusing on signs of OSA. Sleep physicians evaluated videos using the Monash scoring system, blinded to polygraphy (PG) results. PG (Nox T3 device) at home, served as the reference standard, measuring airflow, respiratory patterns, and oxygen saturation. Obstructive apnea hypopnea index (oAHI) and oximetry metrics including oxygen desaturation index 3% (ODI3) and McGill Oximetry Score (MOS) were calculated from the PG. OSA was considered present (mild-to-severe) if oAHI was ≥1.5 events/hour, moderate-to-severe when oAHI≥ 5 events/hour and severe when oAHI was >10 events/hour. ODI3 thresholds >4.3 and >7 events/hour, and MOS ≥2 were used to indicate presence of OSA. Results: 51 patients (45% female) were included in the study. For mild-to-severe OSA, the Monash video score demonstrated a sensitivity of 91.7% and a specificity of 70.4%. For moderate-to-severe OSA, sensitivity was 100% with low specificity (29.6%). ODI ≥ 4.3 had the highest area under the curve (AUC) value of 98.5 (CI 96-100) while AUC was 84.5 (CI 73.1-96.3) for Monash score. Combining video scores with PG oximetry metrics improved diagnostic accuracy, with AUC values reaching 100% across severity thresholds. Conclusion: Although oximetry remains a useful tool for screening and diagnosing OSA in children, home-recorded video clips also have high sensitivity for pediatric OSA screening and may be useful to screen and diagnose children with OSA when access to oximetry, PG or PSG is limited. Using this tool in resource-limited healthcare settings could prioritize at-risk children for further testing, enabling early diagnosis and intervention.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".