Patterns of Using Complementary and Alternative Medicine by Stroke Patients at Two University Hospitals in Korea
Bibliographic record
Abstract
This study measured the prevalence of complementary and alternative medicine (CAM) use among Korean stroke patients. Questionnaire-based 20-min interviews were conducted at the hospitals by a trained nurse after an outpatient visit. It included questions on demographic information, clinical information and the utilization of CAM. Of 304 stroke-patient respondents, 164 (54%) had used CAM, of which 66% had started taking CAM products following suggestions from family members and other relatives. Of the 57% of users who felt that CAM was effective, 84% considered that it improved the symptoms of stroke and 16% felt it was effective in achieving psychological relaxation. Of the eight CAM categories used by respondents, 92% used traditional Oriental medical treatments, 36% used plant- and animal-derived over-the-counter health care products, 24% used minerals and vitamins, and 11% used manual therapies. The majority of stroke patients (68%) were trying a new type of CAM, and half of the respondents (45%) relied on the knowledge of their general practitioner about CAMs when deciding whether to use them. Most of the stroke patients in this study used CAM, and a half of them reported beneficial effects. Despite the presence of adverse side effects, they tended to be used without discussion with chief physicians, and hence physicians should be actively involved in the usage of CAM.
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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 | 0.000 | 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 | 0.001 |
| 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".