Comparison of the EORTC QLQ-C15-PAL and the FACIT-Pal for assessment of quality of life in patients with advanced cancer
Bibliographic record
Abstract
Shorter quality-of-life (QoL) assessments are beneficial for palliative patients as they reduce burden associated with completing personal, and at times stressful, questionnaires. The European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 15 Palliative (QLQ-C15-PAL) and the Functional Assessment of Chronic Illness Therapy - Palliative Care (FACIT-Pal) are two palliative QoL tools that have been validated for use in this population. The purpose of this article was to conduct a review of studies utilizing these two palliative-specific QoL instruments, their development and their relative strengths for use in advanced cancer patients. Studies detailing the development process for the QLQ-C15-PAL and the FACIT-Pal were identified. A comparison between both questionnaires in terms of development, characteristics, validation and use was conducted. The QLQ-C15-PAL was developed via structured shortening of the longer core instrument, the Quality of Life Questionnaire Core 30 (QLQ-C30), whereas the FACIT-Pal includes the Functional Assessment of Cancer Therapy - General tool plus a new 19-item palliative scale created through interviews with patients and healthcare professionals. Although significant overlap exists between both tools, there is a marked difference in the aspects of QoL assessed. Scoring, organization and item format are different; however, response options and recall period are the same. Both tools cover the core items relevant to patients with advanced cancers and can be supplemented with disease-specific tools. Both QLQ-C15-PAL and FACIT-Pal allow for assessment of QoL issues specific to patients with advanced diseases. Each instrument has unique strengths and weaknesses and choice between these tools is dependent on the investigator and study needs. Future studies should directly compare these two tools and validate their use through a number of administration modes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".