Accuracy of Oximetry with Thermistor (OxiFlow) for Diagnosis of Obstructive Sleep Apnea and Hypopnea
Bibliographic record
Abstract
OBJECTIVES: To evaluate the diagnostic accuracy for obstructive sleep apnea and hypopnea (OSAH) of the OxiFlow (OF) device which combines oximetry with recording of thermistor airflow. DESIGN & SETTING: Patients scheduled for overnight diagnostic polysomnography (PSG) were studied with OF either simultaneously during laboratory PSG (L-OF, n=86), at home on a separate night (H-OF, n=66), or both (n=55). PATIENTS: 97 patients with suspected OSAH, of whom 40 had OSAH defined as an apnea-hypopnea index (AHI) of more than 15 events per hour of sleep on PSG. INTERVENTIONS: NA. MEASUREMENTS & RESULTS: The automated respiratory disturbance index (RDI) generated by the OF software considerably underestimated the AHI by PSG for both L-OF and H-OF. Altering the parameters for hypopnea identification by the software did not improve this. Visual inspection of the computerized OF tracings added considerable diagnostic information, but a manual count of RDI during visual review overestimated AHI. For the identification of cases vs. non-cases of OSAH, receiver operating characteristic area-under-the-curve statistics ranged from 0.77-0.90 for L-OF and from 0.71-0.77 for H-OF. Combining automated analysis with subsequent visual inspection of OF tracings yielded an overall sensitivity of 86% and specificity of 74% for the diagnosis of OSAH during H-OF recordings. Analysis of potential technician time saved indicated a benefit from the use of OF. CONCLUSIONS: OF has diagnostic utility for the identification of OSAH. However, because of hardware and software limitations, it is unclear whether this device is superior to oximetry alone.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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 teacher head, 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".