Exercise-based interventions for cancer survivors in India: a systematic review
Bibliographic record
Abstract
Existing literature suggests that cancer survivors present with high rates of morbidity due to various treatment and disease induced factors. Research globally has shown exercise to be beneficial in improving treatment outcomes and quality of life. India has a high prevalence of cancer and not much is known about exercise interventions for cancer survivors in India. This review was planned to review the state of exercise based interventions for cancer survivors in India. A comprehensive literature search was performed in PubMed, CINAHL, EMBASE, Scopus, Cochrane Library, PEDro, IndMed, and Shoda Ganga. The search results were screened and data extracted by two independent reviewers. All eligible studies were assessed for methodological quality rating using Downs and Black checklist. Data was extracted using a pilot tested pro forma to summarize information on site and stage of cancer, type of exercise intervention and outcome measures. The review identified 13 studies, published from 1991 to 2013, after screening 4060 articles. Exercise interventions fell into one of three categories: (1) yoga-based, (2) physiotherapy-based and (3) speech therapy based interventions; and exclusively involved either breast or head and neck cancers. Studies were generally of low to moderate quality. A broad range of outcomes were found including symptoms, speech and swallowing, and quality of life and largely supported the benefits of exercise-based interventions. At present, research involving exercise-based rehabilitation interventions in India is limited in volume, quality and scope. With the growing burden of cancer in the country, there is an immediate need for research on exercise based interventions for cancer survivors within the sociocultural context of India.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| 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".