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Record W2900800431

A Review on the Chemical versus Alternative Treatments of Leukemia

2018· review· en· W2900800431 on OpenAlexaff
Aliya Fatima Mirza

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2018
Typereview
Languageen
FieldChemistry
TopicSynthesis and biological activity
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLeukemiaTraditional medicineMedicineInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.936
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0050.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0290.000

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.

Opus teacher head0.563
GPT teacher head0.601
Teacher spread0.038 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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