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Does change in health-related quality of life (HRQoL) score predict survival? Analysis of a lung cancer RCT.

2012· article· en· W2601373489 on OpenAlexaff
Divine Ediebah, Corneel Coens, Efstathios Zikos, Chantal Quinten, Jolie Ringash, Carolyn Gotay, Elfriede Greimel, John Maringwa, Madeleine King, Hans‐Henning Flechtner, Jospeh Schmucker-Von Koch, Joachim Weis, Bryce B. Reeve, Egbert F. Smit, Andrew Bottomley

Bibliographic record

VenueJournal of Clinical Oncology · 2012
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsUniversity of British ColumbiaPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineGemcitabineInternal medicineLung cancerQuality of life (healthcare)Hazard ratioOncologyProportional hazards modelRandomized controlled trialStatistical significanceCancerConfidence interval

Abstract

fetched live from OpenAlex

7607 Background: Over 60 cancer clinical trials have shown that baseline health-related quality of life (HRQoL) scores are prognostic for patient survival. Few studies have investigated the added value of change in HRQoL scores. Our aim was to investigate if change in HRQoL scores from baseline over time is also associated with survival. Methods: We analyzed data from an EORTC 3-arm randomized clinical trial (RCT) in advanced non-small-cell lung cancer (NSCLC) patients, comparing gemcitabine+cisplatin, versus paclitaxel+gemcitabine, versus standard arm paclitaxel+cisplatin. HRQoL was measured in 394 patients using the EORTC QLQ-C30 at baseline and after each chemotherapy cycle. The prognostic significance of sex, age and WHO performance status (0-1 vs. 2) and the 15 QLQ-C30 subscales were assessed with Cox proportional hazard models stratified for treatment (level of significance 0.05). Changes in HRQoL scores from baseline to each chemotherapy cycle assessment were categorized as “improved”, “stable” and “worsened” using a threshold of 10 points difference. Due to expected attrition, the analysis was limited to changes from baseline up to cycle 3. Results: There were 248 patients in cycle 1, 212 in cycle 2 and 196 in cycle 3. We performed analyses separately using data at cycle 1, cycle 2, and cycle 3. In all analyses, HRQoL in various subscales and socio-demographic and clinical variables (physical functioning (hazard ratio [HR] 0.91, 95% CI 0.85-0.98; p=0.0103), pain (1.11, 1.05-1.17; p= 0.0004), age (0.98, 0.97-1.00, p=0.0413) and WHO performance status (1.77, 1.09-2.89; p=0.0218) at cycle 1; pain (1.11, 1.03-1.20; p=0.0016), age (0.98, 0.96-1.00; p=0.0217) and sex (0.63, 0.42-0.95; p=0.0081) at cycle 2; and role functioning (0.93, 0.88-1.00; p=0.0128) and age (0.98, 0.96-1.00; p=0.0081) at cycle 3) predicted survival; however, change in HRQoL was only an independent predictor for improvement at cycle 1. Conclusions: Our findings suggest that change from baseline over time in HRQoL, as measured on subscales of the EORTC QLQ-C30, contains added prognostic value for survival independent of baseline HRQoL scores. Further work is needed to assess the robustness and sensitivity of these findings.

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)0.089
metaresearch head score (Gemma)0.097
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.911
Threshold uncertainty score0.473

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0890.097
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.013
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.244
GPT teacher head0.580
Teacher spread0.336 · 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.

Study designObservational
DomainMethods
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
Published2012
Admission routes1
Has abstractyes

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