Comparative Analysis of Automated Rules-Based Scoring Versus Human Scoring of Sleep Apnea
Bibliographic record
Abstract
Sleep apnea is a common sleep disorder characterized by repeated interruptions in breathing during sleep, posing significant health risks if left untreated. Accurate detection and scoring of apnea and hypopnea events is crucial for effective diagnosis and treatment of sleep apnea. This study evaluates the performance of an automated scoring algorithm in detecting sleep apnea events compared to human annotators, using full-night recordings of 141 patients. The algorithm applies the American Academy of Sleep Medicine (AASM) criteria to nasal pressure and blood oxygen (SpO2) signals, evaluating each breath in the polysomnogram (PSG) recording. Preliminary results show a 95% agreement on apnea severity, as measured by the Apnea-Hypopnea Index. Our results also indicate that the automated algorithm results in fewer detected apnea events compared to human annotators, and that this trend is inversely related to apnea severity. The study analyzes the patterns of discrepancy between the algorithm and human annotators, analyzing these discrepancies against the stringent AASM criteria. We find that lowering criteria results in greater agreement between our automated scoring algorithm and human annotators. Finally, we consider the use of this algorithm for an automated human-in-the-loop system.
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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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".