Sleep monitoring with portable devices in ICU patients
Bibliographic record
Abstract
Introduction: Sleep disruption and deprivation is a continuing problem in the Intensive Care Unit, but measures to improve sleep cannot utilize traditional polysomnography. Practical, non-intrusive diagnostic monitoring of sleep is required. Aim: To: 1) test two new portable ambulatory sleep diagnostic devices to monitor sleep in ICU and 2) compare sleep data generated by the different devices. Methods: The devices were a) WatchPAT 200 (Itamar Medical), wrist watch-style, employing peripheral arterial tone and actigraphy to evaluate sleep time and sleep stage by an automatic algorithm (PAT device) and b) ALICE PDx (Respironics Philips), miniature polysomnographic device utilizing EEG and EMG recordings, with technician scoring (Mini-PSG device). Both include oximetry and position sensors. Seven ICU patients provided informed consent (mean age 68 years) and were recorded wearing both devices, from 2100 to 0600. Results: Both devices successfully monitored sleep in ICU patients. The PAT device was less intrusive with size and attachments. Saturation and heart rate oximetry data were identical from the devices: Both devices calculated total sleep time (TST), and detected changing sleep stage. There were significant differences in reported values. Mean TST reported was 365 and 507 min., for Mini-PSG and PAT device, respectively, difference 28%. Similarly, REM sleep time was 7.9 and 16.1%TST for the devices, difference 51%. There was large inter-patient variance; some patients showed similar results from both devices. Conclusions: Portable sleep diagnostic devices can successfully monitor sleep in ICU patients. Devices based on different sensor recordings may generate different calculations of sleep time and stage.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".