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 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".