COMPLEMENTARY AND ALTERNATIVE MEDICINE USE IN THE TRANSPLANT PATIENT
Bibliographic record
Abstract
Complementary and alternative medicine (CAM) can be defined as a “group of diverse medical and health care systems, practices, and products that are not generally considered part of conventional medicine.”1 This definition encompasses natural health products (NHPs), including herbal medicines, vitamins and minerals, mind and body medicine and manipulative and body-based practices.1 The use of CAM is increasing in the general population, and continues to rise. The prevalence of CAM usage reported in the literature ranges between 9-65%.2 The prevalence remains high when focusing on the use of NHPs alone. A survey conducted by Health Canada revealed that 71 % of Canadians have used a NHP, with 38 % using a NHP on a daily basis.3 Results from the United States show that 17.7 % of adults use a NHP.1 Several studies exist that explore the use of CAM in solid organ transplant recipients.4,5,6 These studies suggest that while the use of NHPs is high, most preparations are taken without medical consultation and awareness of their toxicities or drug interactions were low. Therefore, knowledge of patient use and the potential effects on transplant recipients is prudent. There is little research on the use of NHPs in combination with immunosuppressant medications. As a result, various NHPs are considered contraindicated or to be used with caution due to theoretical drug-disease and drug-drug interactions. Drug-disease interactions occur when the NHP used stimulates the immune system, putting
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".