Use of Complementary and Alternative Medicine among Osteoarthritic Patients: A Review
Bibliographic record
Abstract
INTRODUCTION: One of the most important indications of complementary and alternative medicines (CAM) is in arthritis. The popularity of CAM in arthritis is consistently on the rise because of the potential side effects of the conventional therapy (Methotrexate) of arthritis. In view of this, it was important to summarize the information, for healthcare professionals and the patients, about the safety and effectiveness of various CAM use in arthritis. MATERIALS AND METHODS: This comprehensive review is based on the content derived through a thorough literature search using 5 electronic databases such as Science direct, Springer link, PubMed, Jet P and Google scholar. Equivalent terms in thesauruses or Medical Subject Heading (MeSH) browsers were used whenever possible. We included all the articles those are used CAM medications for the treatment of arthritis around the globe and searched for the required articles published in English in peer reviewed journals from January 1999 to February 2014. Reports were then arranged and analysed on the basis of country specific studies. RESULTS: Initially, a total of 156 articles were retrieved, after further screening, 27 articles were selected according to meet objectives of the study and those articles which did not qualify, were excluded. Seventeen appropriate studies were finally included in the review. Indeed most of the studies that fulfilled the objective of this review were carried out in US (n=8, 47%), then in India (n=2, 11.76%), UK (n=1, 5.88%), Canada (n=1, 5.88%), Australia (n=1, 5.88%), Korea (n=1, 5.88%), Thailand (n=1, 5.88%), Turkey (n=1, 5.88%) and Malaysia (n=1, 5.88%). CONCLUSION: The review revealed that family, friend, past experiences and lack of effectiveness of conventional therapy are the major factors that influenced patients' decision of initiating and persisting with CAM therapy. The review highlighted the need to conduct future studies by using some more specific health related outcome measures.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".