Comparative Effectiveness of Interventions to Treat Cancer Treatment-Related Cognitive Impairment in Adult Cancer Survivors Following Systemic Therapy: A Systematic Review with Network Meta-Analyses
Bibliographic record
Abstract
BACKGROUND: Cancer treatment-related cognitive impairment (CTRCI) is a frequent and persistent consequence of systemic cancer therapy, adversely affecting quality of life and independence among cancer survivors. METHODS: To clarify the relative effectiveness of available treatments, we conducted a systematic review and network meta-analyses of randomized controlled trials evaluating psychological, pharmacological, and other interventions for established CTRCI in adult survivors of non-central nervous system cancers. Eligible trials reported objective outcomes in one or more of eight cognitive domains, including learning, memory, processing speed, word generation, cognitive flexibility, attention, working memory, and abstraction. RESULTS: = 1100) contributing to network meta-analyses of immediate post-intervention effects across seven domains. A therapist-guided group intervention combining patient education and cognitive rehabilitation consistently ranked highest and was associated with significantly improved learning, memory, processing speed, attention, and working memory compared with a waitlist control, although the certainty of evidence (CoE) was low to very low and largely based on a single trial. Mindfulness-based interventions were also associated with improved processing speed (low CoE). Donepezil was associated with no benefit versus placebo for any domain. CONCLUSIONS: While findings suggest that structured multimodal group interventions may represent the most promising strategy for CTRCI, CoE was low, and additional rigorous, standardized trials are required.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.026 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".