Comparison of positive airway pressure device download data with and without oximetry to polysomnography.
Bibliographic record
Abstract
Background: The apnea-hypopnea index (AHI) detected by positive airway pressure (PAP) devices is frequently utilized in clinical settings; however, its correlation to polysomnography (PSG) is uncertain. Aim: This study compared PAP device data with integrated oximetry, to PSG. Methods: Two device generated datasets were compared to PSG data. Device datasets included 1) Automated PAP device data, and 2) Manually scored events using oximetry and device-detected flow. Results:17 patients (35% female, median age 12.7 [IRQ 9.2-15.1] years, 44% obese) were included. Total recording time of the download was significantly longer than total sleep time as determined by PSG (median 461 [432-480] vs. 406 [331-439] min, p=0.01). The median (IQR) AHI using PSG was 1.4 (0.3, 2.2); using download it was 2.2 (1.0, 5.4); using download with oximetry it was 1.4 (0.5, 2.5). AHI reported by the automated download was significantly higher (Incidence Rate Ratio IRR=1.7, 95%CI 1.4-2.0, p<0.001) when compared to AHI scored on PSG. AHI with download using oximetry and device-detected flow was similar to AHI scored on PSG (IRR= 0.9, 95% CI 0.8-1.1, P=0.3). Of events scored manually based on oximetry and device-detected flow, 30% occurred during wakefulness. 30% of events identified by PSG were associated with arousals. Conclusions: Automated download data did not accurately reflect results of gold standard PSG. Manual scoring of events using oximetry and device-detected flow improved the accuracy of detecting respiratory events, however the discrepancy resulting from events scored during wakefulness, as well as inability to detect events causing arousals, remain sources of error.
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 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".