Identification of Distinct Profiles of Cancer-Related Fatigue and Associated Risk Factors for Breast Cancer Patients Undergoing Chemotherapy
Bibliographic record
Abstract
BACKGROUND: Cancer-related fatigue is a complex, multidimensional, subjective experience that affects patients physically, emotionally, and mentally. The interindividual variability in symptoms of cancer-related fatigue merits further exploration. OBJECTIVE: Our objective was to identify distinct profiles of cancer-related fatigue experienced by breast cancer patients undergoing chemotherapy and to evaluate how subgroups vary demographically in clinical characteristics and in modifiable factors such as physical activity, sleep quality, and exercise self-efficacy. METHODS: Fatigue was assessed with the Chinese Cancer-Related Fatigue Scale, and a latent class analysis was performed to identify subgroups of patients with distinct fatigue profiles. RESULTS: A total of 427 breast cancer patients were included in the data analyses. Five different fatigue profiles were identified: all low-risk fatigue, all high-risk fatigue, high-risk physical fatigue, high-risk emotional fatigue, and high-risk mental fatigue. Patients in different subgroups were characterized by different risk factors. For example, patients in the high-risk emotional fatigue group had less education, lower monthly household incomes, lower exercise self-efficacy scores, less sedentary behavior, poorer sleep, and poorer quality-of-life outcomes compared with those in the all low-risk fatigue group. CONCLUSION: These findings reveal that breast cancer patients undergoing chemotherapy show significant heterogeneity in their experience of cancer-related fatigue. IMPLICATIONS FOR PRACTICE: Characteristics associated with different fatigue profiles, in particular the high-risk profiles, can be used by clinicians to target patients at greater risk of poorer symptom and quality-of-life outcomes to provide interventions tailored to their different needs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".