Validation of Nasal Pressure for the Identification of Apneas/Hypopneas during Sleep
Bibliographic record
Abstract
The reference standard for identifying apneas and hypopneas is a pneumotachograph, but using this can disrupt sleep. Nasal airflow estimation by measuring nasal pressure via nasal prongs is better tolerated by patients. However, nasal pressure has not been validated, using an event-by-event analysis, for detecting apneas/hypopneas during sleep. Eleven patients undergoing polysomnography wore a nasal mask capable of measuring nasal airflow (via pneumotachograph) and nasal pressure simultaneously. Each study was screened for respiratory disturbances, and from these 550 were randomly selected and blindly scored as an apnea/hypopnea or no event each using the pneumotachograph, nasal pressure, square root nasal pressure, and respiratory inductance sum signals independently. Agreement was measured using Cohen's kappa statistic. Intermeasurement agreements between the pneumotachograph and nasal pressure, square root nasal pressure, and respiratory inductance plethysmography sum were 0.76, 0.73, and 0.50, respectively. Inter- and intrarater agreements were, respectively, 0.68 and 0.60 for the pneumotachograph, 0.66 and 0.82 for nasal pressure, 0.61 and 0.78 for square root nasal pressure, and 0.47 and 0.76 for respiratory inductance plethysmography sum. These results indicate that nasal pressure has excellent agreement compared with a pneumotachograph and very good inter-/intrarater agreement. Square root transformation of the nasal pressure signal does not improve these levels of agreement, indicating that it is unnecessary in routine clinical practice for scoring apneas/hypopneas.
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 | 0.037 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".