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Record W4407767997 · doi:10.1188/25.onf.e35-e57

Distinct Morning and Evening Fatigue Profiles in Patients With Gynecologic Cancers Receiving Chemotherapy

2025· article· en· W4407767997 on OpenAlexaff
David Ayangba Asakitogum, Jerry John Nutor, Marilyn J. Hammer, Rachel Pozzar, Bruce A. Cooper, Steven M. Paul, Yvette P. Conley, Jon D. Levine, Christine Miaskowski

Bibliographic record

VenueOncology nursing forum · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsNutrasource
FundersNational Cancer Institute
KeywordsMedicineEveningMorningChemotherapyGynecologic cancerInternal medicineOncologyCancerOvarian cancer

Abstract

fetched live from OpenAlex

OBJECTIVES: To identify distinct morning and evening fatigue profiles in patients with gynecologic cancers and evaluate for differences in demographic and clinical characteristics, common symptoms, and quality-of-life outcomes. SAMPLE & SETTING: Outpatients with gynecologic cancers (N = 233) were recruited before their second or third cycles of chemotherapy at four cancer centers in San Francisco Bay and New York. METHODS & VARIABLES: The Lee Fatigue Scale was completed six times over two cycles of chemotherapy in the morning and in the evening. Latent profile analysis was used to identify distinct morning and evening fatigue profiles. RESULTS: Four distinct morning and two distinct evening fatigue classes were identified. Common risk factors for morning and evening fatigue included younger age, higher body mass index, lower functional status, and higher comorbidity burden. Patients in the worst morning and evening fatigue classes reported higher levels of anxiety, depression, and sleep disturbance; lower levels of energy and cognitive function; and poorer quality of life. IMPLICATIONS FOR NURSING: Clinicians can use this information to identify higher-risk patients and develop individualized interventions for morning and evening fatigue.

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 score0.198
Threshold uncertainty score0.489

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)0.0000.000

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 head0.011
GPT teacher head0.302
Teacher spread0.292 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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