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Record W2047799030 · doi:10.1089/acm.2008.0552

Traditional Chinese Medicine Research and Education in Canada

2009· article· en· W2047799030 on OpenAlexafffundabout
Muhammad Nabeel Ghayur

Bibliographic record

VenueThe Journal of Alternative and Complementary Medicine · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGinseng Biological Effects and Applications
Canadian institutionsHamilton General HospitalSt. Joseph’s Healthcare HamiltonMcMaster University
FundersConnaught FundAcademy of Medical SciencesUniversity of Hong KongTaipei Medical UniversityChinese University of Hong KongUniversity of Ottawa
KeywordsMedicineAlternative medicineTraditional medicineTraditional Chinese medicineMedical educationConstructiveFamily medicinePathology

Abstract

fetched live from OpenAlex

Abstract Traditional Chinese Medicine (TCM) is one of the oldest forms of medicine in the world. There has been a growing interest in TCM in Canada in terms of consumers and also among the research community. To cater for this interest, the Canadian Institute of Chinese Medicinal Research (CICMR) was established in 2004. Since its formation, CICMR has been organizing annual meetings. In 2008, the CICMR meeting, jointly organized with the Ontario Ginseng Innovation Research Centre, was held from October 16th to 19th, in London, Ontario, Canada. The meeting saw a number of participants and speakers from many countries who discussed TCM in a Canadian perspective. The talks and presentations focused on TCM practices in Asia and Canada; analytical techniques for unravelling the science behind TCM; basic and clinical research findings in the areas of cancer and cardiovascular diseases; safety and quality control issues; the regulatory and educational framework of TCM in Canada; and the latest findings in agricultural, chemical, and pharmacological research on ginseng from all over the world. The meeting successfully provided a platform for constructive discussions on TCM practices and research and education in Canada and the world.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.833
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.065
GPT teacher head0.380
Teacher spread0.315 · 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

Citations9
Published2009
Admission routes3
Has abstractyes

Explore more

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