An evidence‐based review of yoga as a complementary intervention for patients with cancer
Bibliographic record
Abstract
OBJECTIVE: To conduct an evidence-based review of yoga as an intervention for patients with cancer. Specifically, this paper reviewed the impact of yoga on psychological adjustment among cancer patients. METHODS: A systematic literature search was conducted between May 2007 and April 2008. Data from each identified study were extracted by two independent raters; studies were included if they assessed psychological functioning and focused on yoga as a main intervention. Using a quality rating scale (range = 9-45), the raters assessed the methodological quality of the studies, and CONSORT guidelines were used to assess randomized controlled trials (RCTs). Effect sizes were calculated when possible. In addition, each study was narratively reviewed with attention to outcome variables, the type of yoga intervention employed, and methodological strengths and limitations. RESULTS: Ten studies were included, including six RCTs. Across studies, the majority of participants were women, and breast cancer was the most common diagnosis. Methodological quality ranged greatly across studies (range = 15.5-42), with the average rating (M = 33.55) indicating adequate quality. Studies also varied in terms of cancer populations and yoga interventions sampled. CONCLUSIONS: This study provided a systematic evaluation of the yoga and cancer literature. Although some positive results were noted, variability across studies and methodological drawbacks limit the extent to which yoga can be deemed effective for managing cancer-related symptoms. However, further research in this area is certainly warranted. Future research should examine what components of yoga are most beneficial, and what types of patients receive the greatest benefit from yoga interventions.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".