Clinical presentation with 『PangYakHapPyon(方藥合編)』 in Korean medicine
Bibliographic record
Abstract
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(辨證論治).So I would like to offer 『PangYakHapPyon(方藥合編)』 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(方藥合編)』 has been used effectively for over 130 years and widely used in the public.In addition, 『PangYakHapPyon(方藥合編)』 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(辨證論治) for diagnosis and treatment is based 『PangYakHapPyon(方藥合編)』.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".