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Record W4402515821 · doi:10.1097/spc.0000000000000724

The EORTC QLQ breast modules and the FACT-B for assessing quality of life in breast cancer patients – an updated literature review

2024· review· en· W4402515821 on OpenAlexaff
A. Wang, Caroline Hircock, Dominic Sferrazza, Ethan Goonaratne, David Cella, Andrew Bottomley, Shing Fung Lee, Adrian Chan, Edward Chow, Henry C. Y. Wong

Bibliographic record

VenueCurrent Opinion in Supportive and Palliative Care · 2024
Typereview
Languageen
FieldMedicine
TopicLymphatic System and Diseases
Canadian institutionsSt. Michael's HospitalMcMaster UniversityHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsBreast cancerMedicineQuality of life (healthcare)CancerMEDLINEMedical physicsPhysical therapyOncologyInternal medicineNursing

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Two commonly used quality of life questionnaires in breast cancer are EORTC QLQ-BR23, the FACT-B, and the extended FACT-B + 4. More recently, the EORTC EORTC QLQ-BR42 was developed. This systematic review compares the various versions of the EORTC QLQ and FACT tools for breast cancer in terms of their content, validity, and psychometric properties. RECENT FINDINGS: Thirty-six studies met the inclusion criteria. All questionnaires have been proven to be valid, reliable and responsive. The provisional EORTC QLQ-BR45 transitioned to the EORTC QLQ-BR42 in Phase IV of its development, which encompasses the side effects associated with the latest breast cancer treatments. Both the EORTC and FACT measures assess physical and mental dimensions of quality of life, with the EORTC measure placing relatively more emphasis on physical content and FACT placing relatively more emphasis on mental (social and emotional) content. The four additional items in the FACT-B + 4 were developed to address arm lymphoedema following axillary surgery. SUMMARY: The development and uptake of quality of life tools are essential in the evaluation of breast cancer treatments. The EORTC QLQ-BR42 and FACT-B are both valid, reliable, and responsive QoL questionnaires.

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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.475
Threshold uncertainty score0.824

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.125
GPT teacher head0.478
Teacher spread0.354 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations2
Published2024
Admission routes1
Has abstractyes

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