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Record W2031999771 · doi:10.5539/gjhs.v1n1p23

Clinical Study on Dahuang Lingxian Decoction against Postoperative

2009· article· en· W2031999771 on OpenAlexvenueno aff
Qianli Tang, Zhongzheng Guan, Shanhui Gao, Xingzhong Wei, Hai Huang, Jianrong Yang, Yuan Yu, Mingwei Huang

Bibliographic record

VenueGlobal Journal of Health Science · 2009
Typearticle
Languageen
FieldMedicine
TopicMedical Research and Treatments
Canadian institutionsnot available
FundersNatural Science Foundation of Guangxi ProvinceNational Natural Science Foundation of China
KeywordsDecoctionMedicineInternal medicineGastroenterologyTraditional medicine

Abstract

fetched live from OpenAlex

Objective: To observe the clinical effect of Dahuang Lingxian Decoction in preventing postoperative recurrence ofcholelithiasis. Methods: 105 patients of cholelithiasis were randomly divided into 3 groups, receiving clinical treatmentand 12 months observation. Respectively, 36 cases in the group of Dahuang Lingxian Decoction were treated withmodified Dahuang Lingxian Decoction. 33 cases in blank group did not take any medicine of relieving gallbladder anddischarging stone except the routine therapy in preoperational period; 36 cases in Xiaoyan Lidan group administeredtablets of Xiaoyan Lidan. Results: After 3 treatment courses, it showed a total effective rate of 97.22% in DahuangLingxian Decoction group, 81.8% in blank group, and 83.3% in Xiaoyan Lidan group. Analyzed by statistics, thecurative effects between 3 groups had obvious differences (P is less than 0.05), and Dahuang Lingxian Decoction group wassignificantly superior to blank group and Xiaoyan Lidan group(P is less than 0.05). Conclusions: Dahuang Lingxian Decoction hadpreferable efficacy on Cholelithiasis and it is worthy of further promotion in clinical application.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.117
GPT teacher head0.548
Teacher spread0.431 · 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 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
Published2009
Admission routes1
Has abstractyes

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