An audit of two night ambulatory sleep polygraphy in community practice
Bibliographic record
Abstract
BACKGROUND: Nightly variations in sleep study measures have not been extensively reported in actual community practice. Such data may better define the natural history of sleep apnea and guide efficient testing practice. AIMS: To audit patient demographics and polygraphy measures in subjects undergoing two rather than one night of ambulatory monitoring. METHODS: Subjects with two nights of technically acceptable diagnostic Remmers Sleep Recorder testing (Sagatech Electronics, Calgary) were selected from studies referred to a web portal for specialist interpretation. Descriptive data and Bland-Altman analyses were done using R (2.15.1-5quantal, ResearchMethods1.4). RESULTS: Only 1 011 of 23 599 (4.3%) subjects had two acceptable nights of polygraphy. The twice tested patients were typical overweight (median 30.9; interquartile range 26.6-35.6 kg/m 2 ), heavy snoring (90.0%), middle-aged (50; 42-57 years), and moderately sleepy (Epworth scale 10; 6-14) men (63.6%) at moderate pre-polygraphy risk of sleep apnea (59.8; 42.0-84.9%). Recording time, snoring index, estimated respiratory disturbance index (RDI, median 10.5 vs. 9.88/h), and time with SpO 2 CONCLUSIONS: Repeat polygraphy was ineffective being: 1.) rarely requested, 2.) usually done in moderate to high risk for sleep apnea subjects, 3.) free of any indirect first night effects, and 4.) associated with some unbiased disagreement in moderately severe sleep apnea.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".