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Record W4386514358 · doi:10.31219/osf.io/d5jwc

The Quadruple Role of Henry C. Lu in the International Communication of Traditional Chinese Medicine

2023· preprint· en· W4386514358 on OpenAlexaboutno aff
Yingwen Cai

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicTraditional Chinese Medicine Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTraditional Chinese medicineEmperorMeaning (existential)MedicineAlternative medicineTraditional medicineClassicsHistoryPhilosophyEpistemologyAncient history

Abstract

fetched live from OpenAlex

Based on the life of Dr. Henry C. Lu as a practitioner of the Traditional Chinese Medicine (TCM hereafter) and his many books on TCM practice and education, this article draws his profile to show his endeavors in the international communication of TCM as a practitioner, scholar, writer, and educator. He earned worldwide fame from A Complete Translation of the Yellow Emperor’s Classic of Internal Medicine and the Difficult Classic, one of the representative English translations of Huangdi Neijing. We summarized the gap between TCM and Western medicine by use of the Acro Diagram or Tai Chi Diagram, Dr. Lu found a clever way to code syndromes and rank them by voting to jump over this gap. Several key concepts in TCM is re-explained and new words are coined by us to convey their exact meaning. Dr. Lu’s comprehensive list of coded syndromes can help TCM practitioners make an objective diagnosis easily, which we explain by the set theory. In 1986, Dr. Lu founded the International College of Traditional Chinese Medicine of Vancouver which was renamed as Tzu Chi International College of Traditional Chinese Medicine in 2015. Together with his Canadian-Chinese colleagues, Dr. Lu was one of the pioneers to strive over decades for the legislation of TCM, making British Columbia the first region in North America to recognize TCM as a medical major with four types of registerable titles equivalent to Western medicine.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score0.475

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.071
GPT teacher head0.338
Teacher spread0.267 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2023
Admission routes1
Has abstractyes

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