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Record W4387949935 · doi:10.1080/08164622.2023.2246480

A review of using Traditional Chinese Medicine in the management of glaucoma and cataract

2023· review· en· W4387949935 on OpenAlexaff
Ka‐Ying Wong, Chau‐Minh Phan, Y.C. Chan, Ailsa Chui-ying Yuen, Huan Zhang, Danyue Zhao, Ka Yin Chan, Chi Wai, Thomas Chuen Lam, Joanne Han Qiao, David Wulff, Alex Hui, Lyndon Jones, Man‐Sau Wong

Bibliographic record

VenueClinical and Experimental Optometry · 2023
Typereview
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTraditional Chinese medicineMedicineAlternative medicineTraditional medicineGlaucomaOptometryOphthalmologyPathology

Abstract

fetched live from OpenAlex

Traditional Chinese Medicine has a long history in ophthalmology in China. Over 250 kinds of Traditional Chinese Medicine have been recorded in ancient books for the management of eye diseases, which may provide an alternative or supplement to current ocular therapies. However, the core holistic philosophy of Traditional Chinese Medicine that makes it attractive can also hinder its understanding from a scientific perspective - in particular, determining true cause and effect. This review focused on how Traditional Chinese Medicine could be applied to two prevalent ocular diseases, glaucoma, and cataract. The literature on preclinical and clinical studies in both English and Chinese on the use of Traditional Chinese Medicine to treat these two diseases was reviewed. The pharmacological effects, safety profile, and drug-herb interaction of selected herbal formulas were also investigated. Finally, key considerations for conducting future Traditional Chinese Medicine studies are discussed.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.152
GPT teacher head0.502
Teacher spread0.350 · 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 designNot applicable
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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