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Record W4225259221 · doi:10.3390/healthcare10050832

Evaluating Patterns and Factors Related to Sleep Disturbances in Prostate Cancer Patients

2022· article· en· W4225259221 on OpenAlexaff
Shalini Mondal, Steve Edwards, Erik Wibowo, Hashim U. Ahmed, Richard J. Wassersug, Jason Ellis, Maximus Isaac, Dagmara Dimitriou, Stephen Mangar

Bibliographic record

VenueHealthcare · 2022
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSleep (system call)Prostate cancerCancerProstateMedicineOncologyInternal medicineComputer science

Abstract

fetched live from OpenAlex

Prostate cancer patients may experience disturbed sleep as a result of their diagnosis or treatment. This study sought to evaluate disturbed sleep and excessive daytime sleepiness in newly diagnosed patients and those receiving androgen deprivation therapy (ADT). This study was conducted with 74 patients. Subjective data using the Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS) and actigraphy data on ADT/ADT-naïve patients were collected. The prevalence of poor sleep quality, determined from PSQI and ESS scores, was 50% and 16.7% respectively. Those on ADT (n = 20) had poorer sleep quality as determined by significantly higher PSQI scores (70 vs. 40% scoring > 5) and were more likely to have poor sleep quality, sleep latency, and sleep efficiency than ADT-naïve patients (n = 40). Actigraphy data showed that ADT patients slept significantly longer (7.7 vs. 6.8 h), experienced a higher Fragmentation Index (48.3 vs. 37.4%), and had longer daytime nap duration (64.1 vs. 45.2 min) than ADT-naïve patients. The use of objective measures such as actigraphy in the clinical arena is recommended and may be used as a valuable tool for research into sleep assessment in prostate cancer patients.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.059
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.036
GPT teacher head0.384
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.

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".

Quick stats

Citations14
Published2022
Admission routes1
Has abstractyes

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