Oxygen Desaturation Index from Nocturnal Oximetry
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Bibliographic record
Abstract
INTRODUCTION: It is impractical to perform polysomnography (PSG) in all surgical patients suspected of having sleep disordered breathing (SDB). We investigated the role of nocturnal oximetry in diagnosing SDB in surgical patients. METHOD: All patients 18 years and older who visited the preoperative clinics for scheduled inpatient surgery were approached for study participation. Patients expected to have abnormal electroencephalographic findings were excluded. All patients underwent an overnight PSG at home with a portable device and a pulse oximeter. The PSG recordings were scored by a certified sleep technologist. The oximetry recordings were processed electronically. RESULT: Four hundred seventy-five patients completed the study: 217 males and 258 females, aged 60 ± 11 years, and body mass index 31 ± 7 kg/m(2). The apnea-hypopnea index (AHI), the average number of episodes of apnea and hypopnea per hour of sleep, was 9.1 (2.8 to 21.4) [median (interquartile range)] and 64% patients had AHI >5. There was a significant correlation between oxygen desaturation index (ODI, hourly average number of desaturation episodes) and cumulative time percentage with SpO(2) <90% (CT90) from nocturnal oximetry, with the parameters measuring sleep breathing disorders from PSG. Compared to CT90, ODI had a stronger correlation and was a better predictor for AHI. The area under receiver operator characteristics curve for ODI to predict AHI >5, AHI >15, and AHI >30 was 0.908 (CI: 0.880 to 0.936), 0.931 (CI: 0.090 to 0.952), and 0.958 (CI: 0.937 to 0.979), respectively. The cutoff value based on the maximal accuracy for ODI to predict AHI >5, AHI >15, and AHI >30 was ODI >5, ODI >15, and ODI >30. The accuracy was 86% (CI: 83%-88%), 86% (CI: 83%-89%), and 94% (CI: 92%-96%), respectively. The ODI >10 demonstrated a sensitivity of 93% and a specificity of 75% to detect moderate and severe SDB. CONCLUSIONS: ODI from a high-resolution nocturnal oximeter is a sensitive and specific tool to detect undiagnosed SDB in surgical patients.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it