Effect of yoga on patients with cancer: our current understanding.
Bibliographic record
Abstract
OBJECTIVE: To determine whether therapeutic yoga improves the quality of life of patients with cancer. DATA SOURCES: Search of MEDLINE database (1950-2010) using key words yoga, cancer, and quality of life. STUDY SELECTION: Priority was given to randomized controlled clinical studies conducted to determine the effect of yoga on typical symptoms of patients with cancer in North America. SYNTHESIS: Initially, 4 randomized controlled clinical studies were analyzed, then 2 studies without control groups were analyzed. Three studies conducted in India and the Near East provided interesting information on methodologies. The interventions included yoga sessions of varying length and frequency. The parameters measured also varied among studies. Several symptoms improved substantially with yoga (higher quality of sleep, decrease in symptoms of anxiety and depression, improvement in spiritual well-being, etc). It would appear that quality of life, or some aspects thereof, also improved. CONCLUSION: The variety of benefits derived, the absence of side effects, and the cost-benefit ratio of therapeutic yoga make it an interesting alternative for family physicians to suggest to their patients with cancer. Certain methodologic shortcomings, including the limited size of the samples and varying levels of attendance on the part of the subjects, might have reduced the statistical strength of the studies presented. It is also possible that the measurement scales used did not suit this type of situation and patient population, making it impossible to see a significant effect. However, favourable comments by participants during the studies and their level of appreciation and well-being suggest that further research is called for to fully understand the mechanisms of these effects.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".