A Review on the Chemical versus Alternative Treatments of Leukemia
Bibliographic record
Abstract
Common treatments for Leukemia, such as chemotherapy, have played a key role in the treatment of this life-threatening disease. However, they are associated with many side effects such as cardiovascular diseases, ocular diseases, lung toxicity, and endocrine dysfunction. The adverse effects of the common treatments are aggressive for elderly patients and patients who are unable to tolerate the treatments, resulting in their lower survival rate. Thus, more than ever before, Leukemia patients are now turning to complementary and alternative treatments. The objective of this study was to review recent literature to compare common and traditional treatments based on efficacy and associated side effects. Chemotherapy and radiation therapy are associated with many side effects, as is stem cell transplantation, which often accompanies these two treatments. Not many studies have been done on alternative, traditional treatments; however, a small number of studies showed that traditional medicine are effective in vitro. Thus, more scientific studies and clinical trials are needed to be done on alternative treatments to find the efficacy, potency and safety of their associated medicines and procedures. As medicinal concepts of the alternative, traditional treatments usually differ from the common medical treatments—although they both have the same curative goal—greater research and communication between traditional medical researchers and practitioners in alternative therapeutic traditions may lead to new and effective medicine with fewer side effects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".