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Record W2157962780 · doi:10.1002/pon.1012

Association of coping style, pain, age and depression with fatigue in women with primary breast cancer

2005· article· en· W2157962780 on OpenAlexaff
Katrin Reuter, Catherine Classen, Joseph A. Roscoe, Gary R. Morrow, Jeffrey J. Kirshner, Richard J. Rosenbluth, Patrick J. Flynn, Kathleen Shedlock, David Spiegel

Bibliographic record

VenuePsycho-Oncology · 2005
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of Toronto
FundersNational Cancer InstituteSchool of Medicine, Stanford UniversityCummings Foundation
KeywordsCoping (psychology)FatalismBreast cancerClinical psychologyMoodAnxietyPsychologyAssociation (psychology)Depression (economics)MedicinePsychiatryCancerInternal medicinePsychotherapist

Abstract

fetched live from OpenAlex

The purpose of this study was to explore the relative contributions of coping, depression, pain and age, in the experience of cancer related fatigue. A total of 353 women treated for primary breast cancer were assessed within one year of diagnosis using the Profile of Mood States, the Hospital Anxiety and Depression Scale and the mini-Mental Adjustment to Cancer Scale. Fatigue was positively associated with depression and pain, but inversely related to age. In contrast to our expectations, fighting spirit was not associated with less fatigue. A relationship between coping style and cancer-related fatigue was found exclusively for 'positive reappraisal', a combination of fighting spirit and fatalism. Detectable only in multivariate analysis together with depression, the results suggest a weak association between coping and fatigue. The relationship between cancer related fatigue, age and coping styles requires further exploration within longitudinal studies.

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)0.003
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
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 head0.012
GPT teacher head0.299
Teacher spread0.287 · 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

Citations75
Published2005
Admission routes1
Has abstractyes

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