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Record W2346577951 · doi:10.1002/pon.4149

A systematic review of sleep in hospitalized pediatric cancer patients

2016· review· en· W2346577951 on OpenAlexaff
So‐Eun Lee, Gaya Narendran, Lianne Tomfohr‐Madsen, Fiona Schulte

Bibliographic record

VenuePsycho-Oncology · 2016
Typereview
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsAlberta Children's HospitalUniversity of CalgaryUniversity of Guelph
Fundersnot available
KeywordsSleep (system call)MedicinePediatric cancerMEDLINESleep disorderPsychiatryPediatricsClinical psychologyCancerInsomniaInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this systematic review was to describe the occurrence of sleep disruptions in pediatric cancer patients and to identify and discuss the factors related to the hospital sleep environment that may be associated with disturbed sleep. METHODS: A total of 108 articles were located in five databases (PubMed, PsychINFO, Medline, CancerLit, and Google Scholar), and seven met our inclusion criteria and formed the basis of this review. RESULTS: Participants ranged from 1 to 18 years (n = 147). Data from objective and subjective assessments of sleep showed that child sleep was disrupted in the hospital when compared to previously established age-related norms. Noise, light levels, and staff room interruptions were associated with decreased total sleep minutes and increased nighttime awakenings. Methodological limitations of the current research as well as potential directions for future research are discussed. CONCLUSIONS: Investigations into the sources of increased sleep difficulties can be used to inform hospital procedures to create a more supportive sleep environment and more effective screening tools for patients who may be at greater risk for sleep difficulties. This may help to minimize the role that hospitalization plays in precipitating and perpetuating chronic sleep disturbances in pediatric cancer patients. Copyright © 2016 John Wiley & Sons, Ltd.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.299
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0110.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.038
GPT teacher head0.423
Teacher spread0.385 · 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 designSystematic review
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

Citations52
Published2016
Admission routes1
Has abstractyes

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