Predictive value of automated oxygen saturation analysis for the diagnosis and treatment of obstructive sleep apnoea in a home-based setting
Bibliographic record
Abstract
BACKGROUND: A portable monitor for the automated analysis of episodic nocturnal oxygen saturation or SpO(2) (the Remmers Sleep Recorder, RSR) has been proposed for the diagnosis of obstructive sleep apnoea-hypopnoea (OSAH). A study was undertaken to compare the diagnostic performance of automated analysis with the manual scoring of polygraphic data by a more comprehensive respiratory monitor (the Suzanne recorder) used simultaneously in their intended home environment. METHODS: The respiratory disturbance indexes of the two monitors were compared in 94 consecutive adult patients suspected of having OSAH and who were deemed eligible for home-based investigation. RESULTS: The RSR overestimated the number of respiratory events associated with a >or=4% fall in SpO(2) by 13% (p<0.005) but underestimated the number of apnoeas and hypopnoeas defined on the basis of respiratory variables alone or their association with a >or=4% fall in SpO(2) or autonomic arousals by 38-48% (p<0.0001). In addition to these significant biases, the limits of agreement in all instances were wide, indicating a poor concurrence between the two monitors. CONCLUSION: The automated analysis of SpO(2) with the RSR cannot be substituted for the manual scoring of polygraphic data with the more comprehensive respiratory monitor in the diagnosis of OSAH in an ambulatory home-based setting.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".