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Sleep Architecture Patterns in Critically Ill Patients and Survivors of Critical Illness: A Retrospective Study

2023· article· en· W4383313071 on OpenAlexaff
Dimitris Georgopoulos, Εumorfia Kondili, Beth Gerardy, Christina Alexopoulou, Maria Bolaki, Magdy Younes

Bibliographic record

VenueAnnals of the American Thoracic Society · 2023
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsUniversity of ManitobaResearch Manitoba
Fundersnot available
KeywordsMedicineDecilePolysomnographyCritically illIntensive care unitCritical illnessSeverity of illnessOdds ratioWakefulnessPolysomnogramPediatricsPsychiatryIntensive care medicineInternal medicineElectroencephalographyStatistics

Abstract

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Abstract Rationale Sleep abnormalities are very frequent in critically ill patients during and after intensive care unit (ICU) stays. Their mechanisms are poorly understood. The odds ratio product (ORP) is a continuous metric (range, 0.0–2.5) of sleep depth measured in 3-second intervals and derived from the relationship of powers of different electroencephalographic frequencies to one another. When expressed as the percentage of epochs within 10 ORP deciles covering the entire ORP range, it provides information about the mechanism(s) of abnormal sleep. Objectives To determine ORP architecture types in critically ill patients and survivors of critical illness who had previously undergone sleep studies. Methods Nocturnal polysomnograms from 47 unsedated critically ill patients and 23 survivors of critical illness at hospital discharge were analyzed. Twelve critically ill patients were monitored also during the day, and 15 survivors underwent subsequent polysomnography 6 months after hospital discharge. In all polysomnograms, each 30-second epoch was characterized by the mean ORP of the 10 3-second epochs. The number of 30-second epochs with mean ORP within each of 10 ORP deciles covering the entire ORP range (0.0–2.5) was calculated and expressed as a percentage of total recording time. Thereafter, each polysomnogram was characterized using a two-digit ORP type, with the first digit (range, 1–3) reflecting increasing degrees of deep sleep (ORP < 0.5, deciles 1 and 2) and the second digit (range, 1–3) reflecting increasing degrees of full wakefulness (ORP > 2.25, decile 10). Results from patients were compared with those from 831 age- and gender-matched community dwellers free of sleep disorders. Results In critically ill patients, types 1,1 and 1,2 (little deep sleep and little or average full wakefulness) dominated (46% of patients). In the community, these types are uncommon (<15%) and seen primarily in disorders that preclude progression to deep sleep (e.g., very severe obstructive sleep apnea). Next in frequency (22%) was type 1,3, consistent with hyperarousal. Day ORP sleep architecture was similar to night results. Survivors had similar patterns, with little improvement after 6 months. Conclusions Sleep abnormalities in critically ill patients and survivors of critical illness result primarily from stimuli that preclude progression to deep sleep or from the presence of a hyperarousal state.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.386
Teacher spread0.353 · 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 designObservational
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".

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Citations12
Published2023
Admission routes1
Has abstractyes

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