Bibliographic record
Abstract
Objective:To explore the therapeutic effect of nicorandil based on routine medication on stable coronary heart disease(CHD).Methods:A total of 100 inpatients diagnosed as stable CHD were enrolled,randomly and equally divided into nicorandil group(received nicorandil 5mg based on routine medication,three times/d)and routine treatment group.After discharge,patients were followed up for six months.Angina pectoris frequency,ECG,serum level of high sensitive C reactive protein(hsCRP)and 6min walking distance(6MWD)were compared between two groups before and after follow up.Results:After six-month follow up,compared with routine treatment group,there were significant improvements in clinic therapeutic effect(48% vs.74%)and ECG therapeutic effect(78% vs.96%)in nicorandil group,P0.05all;compared with before follow-up,there were significant reductions in angina pectoris frequency and serum hsCRP level;and significant rise in 6MWD in both groups,P0.05all;compared with routine treatment group,there were significant reductions in angina pectoris frequency[(10.35±1.51)times/week vs.(9.95±1.65)times/week]and hsCRP level[(1.12±0.51)mg/L vs.(0.95±0.43)mg/L];and significant increase in 6MWD [(342.38±35.64)m vs.(388.64±32.43)m]in nicorandil group,P0.05 all.Conclusion:Nicorandil can effectively reduce the attack number of angina pectoris of stable coronary heart disease and serum hsCRP level,increase exercise tolerance and improve clinical therapeutic effect.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".