Treatment of cholecystitis with Chinese herbal medicines: A systematic review of the literature
Bibliographic record
Abstract
AIM: To analyze the literature on the use of Chinese herbal medicines for the treatment of cholecystitis. METHODS: The literature on treatment of cholecystitis with traditional Chinese medicines (TCM) was analyzed based on the principles and methods described by evidence-based medicine (EBM). Eight databases including MEDLINE, EMbase, Cochrane Central (CCTR), four Chinese databases (China Biological Medicine Database, Chinese National Knowledge Infrastructure Database, Database of Chinese Science and Technology Periodicals, Database of Chinese Ministry of Science and Technology) and Chinese Clinical Registry Center, were searched. Full text articles or abstracts concerning TCM treatment of cholecystitis were selected, categorized according to study design, the strength of evidence, the first author's hospital type, and analyzed statistically. RESULTS: A search of the literature published from 1977 through 2009 yielded 1468 articles in Chinese and 9 in other languages; and 93.92% of the articles focused on clinical studies. No article was of level I evidence, and 9.26% were of level II evidence. The literature cited by Science Citation Index (SCI), MEDLINE and core Chinese medical journals accounted for 0.41%, 0.68% and 7.29%, respectively. Typically, the articles featured in case reports of illness, examined from the perspective of EBM, were weak in both quality and evidence level, which inconsistently conflicted with the fact that most of the papers were by authors from Level-3 hospitals, the highest possible level evaluated based on their comprehensive quality and academic authenticity in China. CONCLUSION: The published literature on TCM treatment of cholecystitis is of low quality and based on low evidence, and cognitive medicine may functions as a useful supplementary framework for the evaluation.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".