MétaCan
Menu
Back to cohort

Mapping the quality of life and unmet needs of urban women with metastatic breast cancer

2005· article· en· W1536833105 on OpenAlexaboutno aff
Sanchia Aranda, Penelope Schofield, LeAnn Weih, Patsy Yates, Donna Milne, Robyn Faulkner, Nicholas Voudouris

Bibliographic record

VenueEuropean Journal of Cancer Care · 2005
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilMedical Research Council
KeywordsQuality of life (healthcare)Breast cancerQuarter (Canadian coin)MedicineFamily medicineGerontologyNeeds assessmentCancerHealth careDiseaseSocial supportNursingPsychologyInternal medicineGeography

Abstract

fetched live from OpenAlex

Enhancing quality of life and reducing the unmet needs of women are central to the successful management of advanced breast cancer. The objective of this study was to investigate the quality of life and support and information needs of urban women with advanced breast cancer. This study was conducted at four large urban hospitals in Melbourne, Australia. A consecutive sample of 105 women with advanced breast cancer completed a questionnaire that contained the European Organization of Research and Treatment of Cancer Quality of Life Q-C30 and the Supportive Care Needs Survey. Between one quarter and a third of the women reported difficulties with their physical, role and social functioning, and a little over a quarter of the women reported poor global health status. Fatigue was a problem for most women. The highest unmet needs were in the psychological and health information domains. Almost no differences in unmet needs were detected when comparing different demographic and disease characteristics of women. Health care providers should routinely monitor the quality of life and needs of women with advanced breast cancer to ensure that appropriate treatment, information or supportive services are made available.

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)0.001
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 score0.322
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.030
GPT teacher head0.289
Teacher spread0.258 · 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

Citations10
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Journal of Cancer CareSame topicCancer survivorship and careFrench-language works237,207