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Record W2265490056

교통사고 환자에 대한 어혈처방과 복진처방의 비교연구

2007· article· ko· W2265490056 on OpenAlexaboutno aff
전태동, 이한실, 홍서영, 허동석, 윤일지, 오민석

Bibliographic record

Venue한방재활의학과학회지 · 2007
Typearticle
Languageko
FieldMedicine
TopicHealthcare and Venom Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHerbAbdominal painSurgeryTraditional medicineMedicinal herbs
DOInot available

Abstract

fetched live from OpenAlex

Objectives : The aim of this study is to make proof of availability for the abdominal diagnosis. This study was designed to compare with the effect of the herb- medication based on abdominal diagnosis and Dangkisoo-san(Dangguixu-san) herb-medication by McGill Pain Questionnaire-Short Form(SF-MPQ) and Pain Disability Index(PDI) in traffic accident patients. Methods : The group for Herb-medication based on abdominal diagnosis consists of 20 patients and the group for Dangkisoo-san(Dangguixu-san) herb-medication consists of 20 patients. The degree of improvement was evaluated By SF-MPQ and PDI after one week treatment. Results : After 1 week treatment there was no significant difference between the two groups in SF-MPQ, PDI scores. Conclusions : The group treated with herb-medication based on abdominal diagnosis is not statistically different from that of Dangkisoo-san(Dangguixu-san) herb-medication group on traffic accident patients. Further study is needed about the effectiveness of the abdominal diagnosis.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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)0.0070.001

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 head0.110
GPT teacher head0.480
Teacher spread0.370 · 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 designNon-randomized trial
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
Published2007
Admission routes1
Has abstractyes

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