Use of alternative medicines in diabetes mellitus
Bibliographic record
Abstract
AIMS: Enormous advances have been made in medical care but more people are still using herbal or alternative remedies. In chronic conditions such as diabetes patients may turn to alternative remedies that have been purported to improve glycaemic control. This study surveyed diabetic and control subjects about their use of all prescribed medication, over-the-counter supplements, and alternative medications. METHODS: Subjects were prospectively contacted in person or by telephone. Five hundred and two diabetic subjects and 201 control subjects were asked to provide details about themselves, their diabetes (for the diabetic subjects) and their use of prescribed medication, over-the-counter supplements and alternative medications. Subjects were asked to rank their assessment of the effectiveness of each medication. Costs were calculated on a per month basis from average prices obtained from five alternative health stores and five chemist shops. RESULTS: Of the diabetic subjects, 78% were taking prescribed medication for their diabetes, 44% were taking over-the-counter supplements and 31% were taking alternative medications. Of the control subjects, 63% were taking prescribed medication, 51% were taking over-the-counter supplements, and 37% were taking alternative medications. Multivitamins, vitamin E, vitamin C, calcium and aspirin were the most commonly used over the counter supplements. Garlic, echinacea, herbal mixtures, glucosamine were the most commonly used alternative medications. Chromium was used only by diabetic subjects and then only rarely. Subjects rated the effectiveness of the alternative medications significantly lower than for prescribed medications but still thought them efficacious. Alternative medications purported to have some hypoglycaemic effect were little used by diabetic subjects. Diabetic subjects spent almost as much money on over-the-counter supplements and alternative medications together as they did on their diabetic medications. CONCLUSIONS: One-third of diabetic patients are taking alternative medications that they consider efficacious but this is no more than in the control group. The money spent on alternative and non-prescription supplements nearly equals that spent on prescription medications. In view of the money spent in this area the time is past due to evaluate these remedies and to establish what merit they have.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".