Assessment of use of complementary alternative medicine and its impact on quality of life in the patients attending rheumatology clinic, in a tertiary care centre in India
Bibliographic record
Abstract
PURPOSE: Complementary and alternative medicine (CAM) has witnessed an increase in use in recent times in rheumatological conditions and is expected to have impact on the quality of life (QOL). We had planned to conduct this study to investigate the extent of use of CAM and its effect on QOL of patients at a tertiary care center. MATERIALS AND METHODS: Ethics committee approval was obtained. Sixty patients suffering from osteoarthritis (OA) and rheumatoid arthritis (RA) were enrolled as per the selection criteria, after obtaining their informed consent. Each patient was interviewed for CAM use/non-use, and Western Ontario and McMaster Universities (WOMAC) (modified) index for QOL was recorded by the study personnel. STATISTICAL ANALYSIS: The normality was checked by using Kolmogorov-Smirnov test. Descriptive statistics was performed and Mann-Whitney U-test was used to compare the QOL of CAM users and non-users. RESULTS: Of the 60 patients enrolled with OA (10) and RA (50), 58% (35/60) used CAM. Ayurveda and massage therapy were the commonest [80% (28/35)], followed by yoga asana [34% (12/35)] and homoeopathy [20% (7/35)]. It was observed that combinations of therapies were used too. Nearly half [49% (17/35)] of the CAM users were on self-prescribed medication and 71% (25/35) of them did not inform the physician of CAM use. The QOL of CAM users (WOMAC score: 56.31 ± 6.82) was better than that of CAM non-users (WOMAC score: 60.16 ± 4.02) (P value 0.01). CONCLUSION: Patients with RA frequently used CAM and QOL improvised with CAM use. We observed that self-administration of CAM was common and this was not informed to the treating physician.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".