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Record W7119015821 · doi:10.24917/25438832.10.13

Analysis of Traditional Medical Sciences in Asia and the Middle East in phytotherapeutic aspects

2025· article· W7119015821 on OpenAlexaff
Mohamad Hesam Shahrajabian

Bibliographic record

VenueAnnales Universitatis Paedagogicae Cracoviensis Studia Naturae · 2025
Typearticle
Language
FieldMedicine
TopicPharmacological Effects of Medicinal Plants
Canadian institutionsBiotechnology Research Institute
Fundersnot available
KeywordsMiddle EastIndigenousKampoTibetan medicineAlternative medicineIslamTurkishFolk medicine

Abstract

fetched live from OpenAlex

Since ancient times, the utilisation of herbs has been common among Indigenous people in different parts of the world. The main important traditional medicine systems are Traditional Chinese medicine (TCM), Traditional Korean Medicine (TKM), Sasang Constitutional Medicine (SCM), Ayurveda, Unani medicine, Kampo (Traditional Japanese Medicine), Traditional Aboriginal Medicine, traditional medicine in Africa, Russian Traditional Medicine (RTM), Iranian Traditional Medicine (ITM), Taditional Arabic and Islamic Medicine (TAIM), Turkish Traditional Medicine (TTM). The aim of this literature review is to provide a brief summary of the various traditional medical sciences in the Middle East and Central Asia using phytotherapies. The information in this article is provided from randomised control experiments, review articles, and analytical studies, as well as observations which gathered from numerous bibliographic sources.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.482
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0030.009
Science and technology studies0.0000.005
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.110
GPT teacher head0.343
Teacher spread0.233 · 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 designObservational
Domainnot available
GenreEmpirical

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
Published2025
Admission routes1
Has abstractyes

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