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Record W2931643975 · doi:10.13048/jkm.19001

Clinical presentation with 『PangYakHapPyon(方藥合編)』 in Korean medicine

2019· article· ja· W2931643975 on OpenAlexaboutno aff
Da Hyun Ju, Byoung Soo Kim

Bibliographic record

VenueJournal of Korean Medicine · 2019
Typearticle
Languageja
FieldMedicine
TopicHealthcare and Venom Research
Canadian institutionsnot available
Fundersnot available
KeywordsPresentation (obstetrics)MedicineStandardizationAlternative medicineMedical prescriptionMEDLINEWestern medicineFamily medicineTraditional Chinese medicineTraditional medicinePathologySurgeryComputer science

Abstract

fetched live from OpenAlex

Objectives: The aim of this study is to introduce the Clinical presentation and announce the importance of developing Clinical presentation of Korean medicine and suggest about development direction of Clinical presentation of Korean medicine. Methods: To Investigate the Clinical presentation used in western medicine. I think that Clinical presentation of Korean medicine is a systematic list of Korean medicine symptoms and a standard syndrome differentiation and treatment(). So I would like to offer PangYakHapPyon() as a basis for developing Clinical presentation of Korean medicine. Results: The clinical presentation term has become widespread in use at Calgary Medical college. Calgary Medical college created a list of 120 clinical presentations In 1991. In Korea, 101 clinical presentations were made in 2016. PangYakHapPyon() has been used effectively for over 130 years and widely used in the public. In addition, PangYakHapPyon() is summarized in the symptoms and prescriptions that occur frequently in Korea. Conclusions: For the globalization and standardization of Korean medicine, Clinical presentation of Korean medicine should be developed. The overall form of Clinical presentation of Korean medicine uses the form of Clinical presentation of Canada and a standard syndrome differentiation and treatment() for diagnosis and treatment is based PangYakHapPyon().

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)0.012
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.095
GPT teacher head0.463
Teacher spread0.368 · 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
Published2019
Admission routes1
Has abstractyes

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