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Record W2000189078 · doi:10.1586/14737167.2014.864560

Predictive factors of overall quality of life in advanced cancer patients using EORTC QLQ-C30

2013· article· en· W2000189078 on OpenAlexafffund
Gemma Cramarossa, Liang Zeng, Liying Zhang, Ling‐Ming Tseng, Ming‐Feng Hou, Alysa Fairchild, Vassilios Vassiliou, Reynaldo Jesús-García, Mohamed A. Alm El‐Din, Aswin Kumar, Fabien Forges, Wei‐Chu Chie, Arjun Sahgal, Henry Lam, Natalie Pulenzas, Edward Chow

Bibliographic record

VenueExpert Review of Pharmacoeconomics & Outcomes Research · 2013
Typearticle
Languageen
FieldMedicine
TopicManagement of metastatic bone disease
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersJoseph and Silvana Melara Cancer Research Fund
KeywordsMedicineQuality of life (healthcare)Predictive valueCancerInternal medicinePhysical therapyOncology

Abstract

fetched live from OpenAlex

OBJECTIVE: To identify which domains/symptoms from the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30) were predictive of overall quality of life (QoL) in advanced cancer patients. METHODS: Four hundred and forty seven patients with brain metastases or bone metastases from seven countries were enrolled with regression analysis to determine the predictive value of the QLQ-C30 functional/symptom scores for patient reported overall QoL (question 30), overall health (question 29) and the global health status domain (questions 29 and 30). RESULTS: Worse role functioning, social functioning, fatigue and financial problems were the most significant predictive factors for worse QoL. In the bone metastases subgroup (n = 400), role functioning, fatigue and financial problems were the most significant predictors. In patients with brain metastases (n = 47), none of the EORTC domains significantly predicted worse QOL. CONCLUSION: Deterioration of certain QLQ-C30 functional/symptom scores significantly contributes to worse QoL, overall health and global health status.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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)0.0030.000

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 head0.097
GPT teacher head0.559
Teacher spread0.462 · 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.

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
Published2013
Admission routes2
Has abstractyes

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