A Review On Influence of Complementary and Alternative Medicine In Type 2 Diabetic Patient
Bibliographic record
Abstract
Complementary and alternative medicine (CAM) refers to a wide range of clinical therapies outside of conventional medicine the term "complementary " refers to the therapy that are used in conjunction with conventional medicine where as alternative medicine includes therapy that are used in place of conventional medicine.The term "integrative medicine that has been advocated by some CAM providers More than one-third of patients with diabetes in the united state use some type of complementary and alternative medicine herbs, dietary supplements and mind body medicine are the most commonly used studies and CAM modalities to treat diabetes including proposed mechanisms a summary of evidence and adverse effect.It also offers recommendation for counseling patient regarding CAM use.The use of CAM for patients with diabetes was reported to be common in almost all parts of the world However, different definitions were used for CAM, which was one of the reasons for a wide range of prevalence of CAM use ranging from 17% to 73% CAM use prevalence in the USA ranged from 31% to 57% among diabetes patients , 63% in Bahrain 62% in Mexico 7% in UK and 25% in Canada China had a long tradition of use of herbal medicine for diabetes The findings of a systematic review reported that Chinese herbal medicines were reported to be more effective for diabetes compared with lifestyle modification alone In China, traditional medicines accounts for 40% of all healthcare.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".