Evaluation of the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 15 Palliative Care and the Functional Assessment of Chronic Illness Therapy-Palliative in assessing the quality of life in patients with advanced cancer
Bibliographic record
Abstract
PURPOSE OF REVIEW: Two widely validated health-related quality of life (HR-QoL) tools, specifically designed for patients with advanced cancer, are the European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire Core 15 Palliative Care (EORTC QLQ-C15-PAL) and the Functional Assessment of Chronic Illness Therapy-Palliative (FACIT-Pal-14). This systematic review aims to evaluate the use of EORTC QLQ-C15-PAL and FACIT-Pal-14 in prospective studies in patients with advanced cancer, focusing on study types, clinical settings, additional HR-QoL tools used, and completion rates. RECENT FINDINGS: Sixty studies were included in the analysis. Both EORTC QLQ-C15-PAL and FACIT-Pal-14 are used in a variety of studies. Given that EORTC QLQ-C15-PAL was developed 9 years before FACIT-Pal-14 PAL, most studies utilized the EORTC tool. Both tools were shown to be successfully used in a variety of clinical settings, including in various advanced tumour types or different study designs, depending on the investigator and study needs. SUMMARY: This review demonstrates the wide range of utilization of EORTC QLQ-C15-PAL and FACIT-Pal-14 in prospective studies to assess the HR-QoL issues in patients with advanced cancers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".