Fatigue and Co‐Occurring Cancer‐Related Symptoms in Breast Cancer Survivors: A Systematic Review and Network Meta‐Analysis
Bibliographic record
Abstract
Objective: The relative strength of risk factors for cancer‐related fatigue (CRF) among breast cancer survivors (BCSs) remains unclear. This study aims to systematically evaluate and compare the strength of different risk factors for CRF using a network meta‐analysis (NMA) approach. Methods: This NMA included observational studies on female BCSs with extractable data related to risk factors for CRF. The PubMed, Cochrane Library, and Embase databases were systematically searched, and the study protocol was registered in PROSPERO (reference no. CRD42025642021). A random‐effects meta‐analysis was performed to estimate pooled effect sizes, and an NMA with P‐scores was used to rank the relative strength of risk factors. Subgroup analyses, sensitivity analyses, and meta‐regression were conducted to assess methodological quality and explore potential sources of heterogeneity. Results: Thirty observational studies ( n = 36,302 female BCSs) that were published between 2004 and 2024 were included in this NMA. Depression exhibited the strongest association with CRF (OR = 3.34, 95% CI 2.50–4.46, P‐score = 0.9727), followed by insomnia (OR = 2.35, 95% CI 1.45–3.81, P‐score = 0.6549), pain (OR = 1.94, 95% CI 1.33–2.84, P‐score = 0.4587), and anxiety (OR = 1.85, 95% CI 1.23–2.79, P‐score = 0.4132). Subgroup analysis revealed that the associations of the four risk factors with CRF remained significant at the three posttreatment time points (< 1 year, 1–5 years, and > 5 years), with the exception of anxiety and insomnia at < 1 year and pain at > 5 years. Meta‐regression demonstrated that higher study quality (measured via the Newcastle–Ottawa scale [NOS]) was significantly correlated with stronger associations of anxiety and insomnia with CRF ( β = 0.305 and 0.221, p < 0.05, respectively). Sensitivity analysis confirmed the robustness of the main findings. Conclusion: Depression plays a central role in CRF development and should be prioritized in survivorship care. Integrating multimodal interventions for depression, sleep disturbances, and pain management may improve fatigue outcomes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".