[An investigation on clinical studies of TCM in preventing and treating angina pectoris of coronary heart disease].
Bibliographic record
Abstract
OBJECTIVE: To analyze the current status of clinical studies of TCM in preventing and treating angina pectoris of coronary heart disease. METHODS: A statistical analysis of articles regarding the use of TCM in preventing and treating angina pectoris, published in TCM core journals or journals of TCM university (college) from January 2001 to June 2002 was conducted, the items analyzed included the differentiation of stable angina (SA) and unstable angina (UA), the grading or stratifying, standard for therapeutic efficacy evaluation, standardized drug therapy of UA (according to the "Suggestion on the diagnosis and treatment of UA" formulated by Society of Cardiovascular Disease, Chinese Medical Association, etc. RESULTS: From the 44 articles that retrieved, UA and SA was not differed in 29 articles (65.9%), among which 11 articles came from provincial, national TCM institute or hospital affiliated to TCM university (college). In the 34 articles dealing with UA, only 3 articles mentioned the standardized drug therapy. Standard of therapeutic efficacy evaluation announced in 1979 was used in 35 articles (79.5%). CONCLUSION: Most articles dealing with clinical study on TCM prevention and treatment of angina pectoris, UA and SA, have the flaws of un-standardized, lacking in compact and insufficient science. Improvement of related standard for clinical therapeutic efficacy evaluation needs to be further perfected.
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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 | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".