Afraid and tired: A longitudinal study of the relationship between <scp>cancer‐related</scp> fatigue and fear of cancer recurrence in <scp>long‐term</scp> cancer survivors
Bibliographic record
Abstract
OBJECTIVE: Cancer-related fatigue (CRF) and fear of cancer recurrence (FCR) are two common concerns experienced by cancer survivors. However, the relationship between these two concerns is poorly understood, and whether CRF and FCR influence each other over time is unclear. METHODS: Data were from a national, prospective, longitudinal study, the American Cancer Society's Study of Cancer Survivors-I (SCS-I). Surveys were completed by 1395 survivors of 10 different cancer types at three time-points, including assessment 1.3 years (T1), 2.2 years (T2) and 8.8 years (T3) following their cancer diagnosis. CRF was assessed using the fatigue-inertia subscale of the Profile of Mood States, and FCR by the FCR subscale of the Cancer Problems in Living Scale. Multiple group random intercepts cross-lagged panel models investigated prospective associations between CRF and FCR. RESULTS: For younger participants (at or below median age of 55 years, n = 697), CRF at T1 and T2 marginally and significantly predicted FCR at T2 and T3, respectively, but no lagged effects of FCR on subsequent CRF were observed. Cross-lagged effects were not observed for survivors over 55 years of age. CONCLUSION: Both CRF and FCR are debilitating side effects of cancer and its treatments. Given that CRF may be predictive of FCR, it possible that early detection and intervention for CRF could contribute to lowering FCR severity.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".