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Record W4399388236 · doi:10.1097/mcc.0000000000001163

Light, sleep and circadian rhythm in critical illness

2024· article· en· W4399388236 on OpenAlexaff
Heather Perry, Athina Alight, M. Elizabeth Wilcox

Bibliographic record

VenueCurrent Opinion in Critical Care · 2024
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsWomen and Children’s Health Research InstituteUniversity of Alberta
Fundersnot available
KeywordsCircadian rhythmMedicineIntensive care medicineRandomized controlled trialObservational studyDark therapyEntrainment (biomusicology)PopulationDeliriumRhythmInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Sleep and circadian disruption (SCD) are associated with worse outcomes in the ICU population. We discuss sleep, circadian physiology, the role of light in circadian entrainment and its possible role in treating SCD, with special attention to the use of light therapies and ICU design. RECENT FINDINGS: The American Thoracic Society recently published an official research statement highlighting key areas required to define and treat ICU SCD. Recent literature has been predominantly observational, describing how both critical illness and the ICU environment might impair normal sleep and impact circadian rhythm. Emerging consensus guidance outlines the need for standardized light metrics in clinical trials investigating effects of light therapies. A recent proof-of-concept randomized controlled trial (RCT) showed improvement in delirium incidence and circadian alignment from ICU room redesign that included a dynamic lighting system (DLS). SUMMARY: Further investigation is needed to define the optimal physical properties of light therapy in the ICU environment as well as timing and duration of light treatments. Work in this area will inform future circadian-promoting design, as well as multicomponent nonpharmacological protocols, to mitigate ICU SCD with the objective of improving patient outcomes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.709
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.078
GPT teacher head0.426
Teacher spread0.348 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations7
Published2024
Admission routes1
Has abstractyes

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