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Record W2939783951 · doi:10.1093/sleep/zsz067.826

0828 Pre-Treatment Insomnia Symptoms and Perceived Cognitive Impairment in Newly Diagnosed Women with Early Stage Breast Cancer

2019· article· en· W2939783951 on OpenAlexafffund
Sheila N. Garland, Joshua A. Rash, Nicole Rodriguez, Ryan H. Collins, Joy McCarthy, Melanie Seal, Kara Laing

Bibliographic record

VenueSLEEP · 2019
Typearticle
Languageen
FieldMedicine
TopicCancer-related cognitive impairment studies
Canadian institutionsMemorial University of Newfoundland
FundersBeatrice Hunter Cancer Research InstituteCancer Research Institute
KeywordsInsomniaPittsburgh Sleep Quality IndexBreast cancerMoodMedicineAnxietyDepression (economics)Clinical psychologyPhysical therapyInternal medicinePsychiatryCancerSleep quality

Abstract

fetched live from OpenAlex

Insomnia and perceived cognitive impairment (PCI) are prevalent in breast cancer (BCa) survivors. These symptoms are often attributed to cancer treatment; however, recent evidence suggests that insomnia and PCI are present before treatment initiation. This study explores the associations between insomnia, PCI, sleep, mood, and fatigue in pre-treatment women with BCa. This study is part of a larger ongoing prospective study of sleep and cognition in women with non-metastatic breast cancer. Participants completed the Insomnia Severity Index (ISI), the Functional Assessment of Cancer Treatment-Cognition (FACT-Cog), the Hospital Anxiety and Depression Scale (HADS), the Pittsburgh Sleep Quality Index (PSQI), and the Multidimensional Fatigue Symptom Inventory-Short Form (MFSI-SF). Using participant’s pre-treatment data, a hierarchical regression model was used to examine associations between symptoms of insomnia, mood, fatigue, and PCI, after statistically adjusting for age and education. Zero-order and partial correlations were used to examine the importance of individual predictors. Data was collected prior to initiating cancer treatment from 86 women newly diagnosed with BCa. On average, women were 59 years old (range 30-80) and had 14 years of education (range 7-25). After adjusting for age and education, the model was significant [F(5, 76)=8.94, p<.001], accounting for 36% of the unique variance in PCI. Zero-order correlations indicated significant bivariate associations between PCI and insomnia severity (r=-.34), sleep quality (r=-.39), fatigue (r=.56) and depressed (r=.38), anxious mood (r=.37). After partitioning out variability from other independent variables, only fatigue remained significantly associated with PCI, accounting for 15.3% unique variance. A follow up hierarchical regression revealed that mental and general fatigue were the dimensions of fatigue significantly associated with PCI. Even before undergoing cancer treatment, sleep, mood, and fatigue are associated with PCI in pre-treatment women with BCa. Fatigue, particularly general fatigue (e.g. I am worn out) and mental fatigue (e.g. I make more mistakes than usual), have the strongest relationships with PCI. Dr. Garland is supported by a New Investigator Award and seed funding from the Beatrice Hunter Cancer Research Institute (BHCRI).

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)0.003
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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.251
Teacher spread0.245 · 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".

Quick stats

Citations0
Published2019
Admission routes2
Has abstractyes

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