Sleep on the ward in intensive care unit survivors: a case series of polysomnography
Bibliographic record
Abstract
BACKGROUND: Few studies have investigated sleep in patients after intensive care despite the possibility that inadequate sleep might further complicate an acute illness impeding recovery. AIMS: To assess the quality and quantity of a patient's sleep on the ward by polysomnography (PSG) within a week of intensive care unit (ICU) discharge and to explore the prevalence of key in-ICU risk factors for persistent sleep fragmentation. METHODS: We enrolled 20 patients after they have been mechanically ventilated for at least 3 days and survived to ICU discharge. We included all patients over the age of 16 years and excluded patients with advanced cognitive impairment or who were unable to follow simple commands before their acute illness, primary admission diagnosis of neurological injury, uncontrolled psychiatric illness or not fluent in English. RESULTS: Twenty patients underwent an overnight PSG recording on day 7 after ICU discharge (SD, 1 day). ICU survivors provided 292.8 h of PSG recording time with median recording times of 16.8 h (Interquartile range (IQR), 15.0-17.2 h). The median total sleep time per patient was 5.3 h (IQR, 2.6-6.3 h). In a multivariable regression model, postoperative admission diagnosis (P = 0.04) and patient report of poor ICU sleep (P = 0.001) were associated with less slow-wave (restorative) sleep on the wards after ICU discharge. CONCLUSIONS: Patients reported poor sleep while in the ICU, and a postoperative admission diagnosis may identify a high-risk subgroup of patients who may derive greater benefit from interventions to improve sleep hygiene.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".