Comparing FACIT-fatigue and EORTC QLQ-FA12 for assessing the quality of life in people with cancer-related fatigue
Bibliographic record
Abstract
PURPOSE OF REVIEW: Two common quality of life (QoL) questionnaires for cancer-related fatigue (CRF) are the European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire Fatigue 12 (QLQ-FA12) and Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-Fatigue). This systematic review compared their content, validity, and psychometric properties. RECENT FINDINGS: Twenty-four studies were included. The QLQ-FA12 (12 items) provides physical, emotional, and cognitive subscales, while the FACIT-Fatigue (13 items) captures self-reported fatigue and its impact on daily function. Both instruments demonstrated validity, internal consistency, test-retest reliability, and sensitivity. Construct validity was supported by correlations with QoL and physical-function measures, and confirmatory factor analyses upheld their intended dimensional structures. The QLQ-FA12's 4-point question format offers distinct domain scores, whereas the FACIT-Fatigue's 5-point statement format yields a single total score. The QLQ-FA12 is preferred when a multidimensional profile is needed, such as in trials addressing specific fatigue drivers or pairing with QLQ-C30 domains. The FACIT-Fatigue suits brief screening or large-scale studies where efficiency and a single total fatigue score are priorities. SUMMARY: The EORTC QLQ-FA12 and FACIT-Fatigue are both sufficiently validated for assessing CRF-related QoL. The QLQ-FA12 is more appropriate when a multidimensional profile is required, whereas FACIT-Fatigue suits contexts needing a unidimensional total severity score.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".