Sleep continuity: a new metric to quantify disrupted hypnograms in non-sedated intensive care unit patients
Bibliographic record
Abstract
INTRODUCTION: Sleep in intensive care unit (ICU) patients is severely altered. In a large proportion of critically ill patients, conventional sleep electroencephalogram (EEG) patterns are replaced by atypical sleep. On the other hand, some non-sedated patients can display usual sleep EEG patterns. In the latter, sleep is highly fragmented and disrupted and conventional rules may not be optimal. We sought to determine whether sleep continuity could be a useful metric to quantify the amount of sleep with recuperative function in critically ill patients with usual sleep EEG features. METHODS: We retrospectively reanalyzed polysomnographies recorded in non-sedated critically ill patients requiring non-invasive ventilation (NIV) for acute hypercapnic respiratory failure. Using conventional rules, we built two-state hypnograms (sleep and wake) and identified all sleep episodes. The percentage of time spent in sleep bouts (<10 minutes), short naps (>10 and <30 minutes) and long naps (>30 minutes) was used to describe sleep continuity. In a first study, we compared these measures regarding good (NIV success) or poor outcome (NIV failure). In a second study performed on a different patient group, we compared these measurements during NIV and during spontaneous breathing. RESULTS: While fragmentation indices were similar in the two groups, the percentage of total sleep time spent in short naps was higher and the percentage of sleep time spent in sleep bouts was lower in patients with successful NIV. The percentage of total sleep time spent in long naps was higher and the percentage of sleep time spent in sleep bouts was lower during NIV than during spontaneous breathing; the level of reproducibility of sleep continuity measures between scorers was high. CONCLUSIONS: Sleep continuity measurements could constitute a clinically relevant and reproducible assessment of sleep disruption in non-sedated ICU patients with usual sleep EEG.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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) | 0.001 | 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".