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Sleep disruption in the intensive care unit

2001· review· en· W2080044055 on OpenAlexaff
Jonathan Y. Gabor, Andrew Cooper, Patrick J. Hanly

Bibliographic record

VenueCurrent Opinion in Critical Care · 2001
Typereview
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicineSleep (system call)Intensive care unitIntensive care medicineCritically illIntensive careEmergency medicine

Abstract

fetched live from OpenAlex

Subjective and objective measures of sleep quality indicate that the sleep of patients in the intensive care unit (ICU) is extraordinarily disturbed. Several studies spanning the past two decades have demonstrated that critically ill patients exhibit reduced sleep efficiency, reduced restorative sleep, and frequent arousals and awakenings. A number of potential sleep disrupters exist in the ICU environment, with noise being the predominant focus of investigation. Excessive noise levels in the ICU correlate with poor sleep quality in healthy subjects and patients. Medications, light, and frequent care-related activities can also interfere with a patient's ability to obtain good-quality sleep. Sleep disruption can have significant adverse consequences for critically ill patients, such as immune system compromise and respiratory abnormalities. Although several questions remain unanswered, including the impact of sleep disruption on the clinical outcome of patients in the ICU, there is a growing interest in developing new strategies to improve sleep quality.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.261
GPT teacher head0.509
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations129
Published2001
Admission routes1
Has abstractyes

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