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Record W4388626680 · doi:10.3389/jpps.2023.11863

Traditional Chinese medicine for treating aplastic anemia

2023· review· en· W4388626680 on OpenAlexvenueno aff
Jing Guan, YiHui Zhao, Ting Wang, Rong Fu

Bibliographic record

VenueJournal of Pharmacy & Pharmaceutical Sciences · 2023
Typereview
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsnot available
Fundersnot available
KeywordsTraditional Chinese medicineMedicineAplastic anemiaBone marrowHaematopoiesisDiseaseAnemiaInternal medicineStem cellTraditional medicineIntensive care medicineOncologyImmunologyAlternative medicinePathologyBiology

Abstract

fetched live from OpenAlex

Aplastic anemia (AA) is a bone marrow failure disease caused by T cell hyperfunction. Although the overall response rate has been improved by immunosuppressive therapy (IST) plus Eltrombopag, 30% of patients have either no response or relapse. We therefore attempted to find other ways to improve the outcomes of AA patients. Traditional Chinese medicine has the advantages of low cost, reasonable effects, and few side effects. More and more clinical studies have confirmed that traditional Chinese medicine has a beneficial role in treating AA patients. This article reviews the potential mechanism of traditional Chinese medicine or its active ingredients in the treatment of AA. These include improving the bone marrow microenvironment, regulating immunity, and affecting the fate of hematopoietic stem cells. This provides useful information for further treatment of AA with integration of traditional Chinese and Western medicine and the development of new treatment strategies.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.326
GPT teacher head0.516
Teacher spread0.190 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations9
Published2023
Admission routes1
Has abstractyes

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