Tai Chi for cancer survivors: A systematic review toward consensus‐based guidelines
Bibliographic record
Abstract
To manage acute, long-term, and late effects of cancer, current guidelines recommend moderate-to-vigorous intensity aerobic and resistance exercise. Unfortunately, not all cancer survivors are able or willing to perform higher intensity exercise during difficult cancer treatments or because of other existing health conditions. Tai Chi is an equipment-free, multicomponent mind-body exercise performed at light-to-moderate intensity that may provide a more feasible alternative to traditional exercise programs for some cancer survivors. This systematic review evaluated the therapeutic efficacy of Tai Chi across the cancer care continuum. We searched MEDLINE/PubMed, Embase, SCOPUS, and CINAHL databases for interventional studies from inception to 18 September 2020. Controlled trials of the effects of Tai Chi training on patient-reported and objectively measured outcomes in cancer survivors were included. Study quality was determined by the RoB 2 tool, and effect estimates were evaluated using the Best Evidence Synthesis approach. Twenty-six reports from 14 trials (one non-randomized controlled trial) conducted during (n = 5) and after treatment (after surgery: n = 2; after other treatments: n = 7) were included. Low-level evidence emerged to support the benefits of 40-60 min of thrice-weekly supervised Tai Chi for 8-12 weeks to improve fatigue and sleep quality in cancer survivors. These findings need to be confirmed in larger trials and tested for scaling-up potential. Insufficient evidence was available to evaluate the effects of Tai Chi on other cancer-related outcomes. Future research should examine whether Tai Chi training can improve a broader range of cancer outcomes including during the pre-treatment and end of life phases.
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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 | 0.046 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".