0731 What is HSAT Missing? A Comparison of Respiratory Events and OSA Diagnosis Across Type 2 and Type 3 Studies
Bibliographic record
Abstract
Abstract Introduction The need for having in-home sleep testing has grown due to the COVID-19 pandemic. While Type 3 Home Sleep Apnea Tests (HSAT) are frequently used, their accuracy remains questionable. This study aimed to compare respiratory events and diagnosis of obstructive sleep apnea between Type 2 and Type 3 studies. Methods 550 participants completed overnight Type 2 sleep studies using the Cerebra Sleep System. Files were autoscored as a type 2 acquisition and were manually edited by a RPSGT. On a second auto-score, mapped file channels were reduced to nasal cannula, chest belt, SpO2, position, heart rate, and audio channels to simulate a Type 3 study. The respiratory disturbance index (RDI) in the Type 2 tests was compared to the apnea-hypopnea index (AHI) in the simulated Type 3 files using a 4% desaturation threshold. Diagnosis of severity of OSA was classified based on indices of <5 as “None”, 5-14.99 as “Mild”, 15-29.99 as “Moderate”, and above 30 as “Severe”. Results 5 records were removed for having a TST <4 hours. Type 2 sleep tests detected significantly more respiratory events (21.0±21.2/hr.) compared to Type 3 tests (13.4 ±17.2; t(549) = 26.8, p<.0001). The use of the Type 2 RDI resulted in 104 patients (18.9% of patients; 39.4% of treatable patients) with moderate OSA falling into the mild category under the Type 3 AHI. The number of treatable patients was thus 71% higher with a Type 2 study. Overall, the diagnoses of Type 2 RDI and Type 3 AHI were only in agreement for 263 out of the 550 records, or 47.8% of the time. Conclusion The use of a Type 2 study detected more respiratory events than the Type 3 device. Consequentially, 104 patients received a higher severity of obstructive sleep apnea when the EEG information was included. Our results provide support for the use of Type 2 devices for in-home detection of obstructive sleep apnea to provide more accurate diagnostic detection than the more frequently used Type 3 home sleep apnea tests. Support (If Any)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.000 | 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".