Health-related quality of life measures in routine clinical care: Can FACT-Fatigue help to assess the management of fatigue in cancer patients?
Bibliographic record
Abstract
OBJECTIVES: Fatigue is the most common symptom reported by cancer patients. The inclusion of health-related quality of life (HRQL) measures in routine clinical care of cancer patients may improve the management of fatigue. The primary objective of this study is to provide evidence on the magnitude of change in fatigue subscale scores using the Functional Assessment of Cancer Therapy-Fatigue (FACT-F) that is clinically important. METHODS: Consecutive patients with advanced primary lung cancer attending a Canadian tertiary care cancer and, prior to undergoing palliative chemotherapy, were enrolled in the study. Patients completed a battery of questionnaires [FACT-F, Qualitative Patients Self-report of Fatigue Level (QPSRF)] at baseline, follow-up and 2 weeks after their final cycle of chemotherapy. Clinicians assessed the patients using the Eastern Cooperative Oncology Group (ECOG) Performance Status Scale at baseline and each follow-up visit. FACT-F change scores were computed as the mean change in score (end of study score minus baseline score). RESULTS: A total of 43 patients with mean age of 59 years were enrolled in the study. Results revealed a mean change in FACT-F subscale score of 5.0 (SE 1.06) for those who rated themselves as more tired, 1.28 (SE 1.00) for those who rated themselves as the same (no change), and -1.52 (SE 0.84) for those patients who rated themselves as less tired. CONCLUSIONS: We provide evidence on the magnitude of change in FACT-F score that is associated with the perception by patients of improvement in fatigue and magnitude of change in score that is associated with worsening in fatigue.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".