Improving Cancer-Related Cognitive Impairment: A Multi-Dimensional Rehabilitative Approach
Bibliographic record
Abstract
INTRODUCTION: Cancer-related cognitive impairment (CRCI), fatigue, reduced physical capacity, and diminished quality of life are common concerns for cancer survivors. Although CRCI was identified as an area for clinical research in 1998, to date, few interventions have addressed this concern. CRCI symptoms include: mental fogginess, concentration and short term memory loss, difficulty multitasking, and fatigue which can persist post treatment. Determining the best approach for a cancer survivor rehabilitation program is needed to help patients make the transition from cancer treatment to optimal health. PURPOSE: To evaluate the effectiveness of a multi-dimensional cancer survivor rehabilitation program on cognition, quality of life, fatigue, body composition and strength. METHODS: Seventy-three post-treatment cancer survivors, (6 men, 67 women, 35-77 years) were subjects in a one group, pre-post, quasi-experimental design. Subjects were cleared for exercise by their primary oncologist. The program consisted of two, 90-minute sessions per week for 12 weeks. Each meeting included 3 parts: an educational activity, cardiovascular training, and strength training. A subset of this group (n = 32) participated in brain exercise games once per week for 15 minutes, with a focus on memory, processing speed, conceptualization, language, reasoning, and problem-solving skills. Emphasis was placed on games requiring focus with the inclusion of a physical task. Dependent measures included: the Montreal Cognitive Assessment (MoCA), the General Practitioner Assessment of Cognition (GPCOG), body fat percentage, handgrip strength, quality of life, Schwartz Cancer Fatigue scale and LASA scale. RESULTS: Significant improvements (p < 0.05): MoCA increased 4.2%, GPCOG increased 8.1%, body fat decreased 5.8%, handgrip increased 12.9%. Quality of life increased 9.9%. Fatigue decreased 17.4%, LASA fatigue 33.1%, depression 47.9%, confusion 41.6%, anxiety 25.3% and anger 51.5% all decreased, and energy increased 26.1%. CONCLUSION: This program proved safe and effective in improving cognition, fatigue, and overall quality of life in cancer survivors. Improvement in cognition is a novel finding because it suggests this debilitating concern may be improved with a multidimensional rehabilitative approach.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".