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Record W4402516089 · doi:10.1097/spc.0000000000000725

Comparing the EORTC QLQ-LC13, EORTC QLQ-LC29, and the FACT-L for assessment of quality of life in patients with lung cancer - an updated systematic review

2024· review· en· W4402516089 on OpenAlexaff
Caroline Hircock, Alyssa J. Wang, Ethan Goonaratne, Dominic Sferrazza, Andrew Bottomley, David Cella, Shing Fung Lee, Adrian W. Chan, Edward Chow, Henry C. Y. Wong

Bibliographic record

VenueCurrent Opinion in Supportive and Palliative Care · 2024
Typereview
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsSt. Michael's HospitalHealth Sciences CentreSunnybrook Health Science CentreMcMaster University
Fundersnot available
KeywordsMedicineLung cancerQuality of life (healthcare)CancerOncologyInternal medicineClinical trial

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Two commonly used quality of life (QoL) questionnaires in lung cancer patients are the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire Lung Cancer 13 (QLQ-LC13) and the Functional Assessment of Cancer Therapy-Lung (FACT-L). More recently, the EORTC QLQ-LC29 was developed. This systematic review compares the EORTC QLQ-LC29, EORTC QLQ-LC13 and FACT-L in terms of the content, validity and psychometric properties in assessing the QoL of lung cancer patients. RECENT FINDINGS: Fourteen studies were included. The EORTC QLQ-LC29 is a 29-item scale that serves as an update of the EORTC QLQ-LC13 to include symptoms from surgery and new targeted therapies. It shows validity, high internal consistency, test-retest reliability, and sensitivity. The FACT-L continues to assess general quality of life and lung cancer-specific symptoms. SUMMARY: The EORTC QLQ-LC29, EORTC QLQ-LC13, and FACT-L were reviewed to assess their validity in measuring QoL of lung cancer patients. All were found to be sufficiently validated, The choice of which to use should depend on the primary goals of the study.

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: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.111
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.198
GPT teacher head0.500
Teacher spread0.302 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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