100 Positive Patients of Treadmill Exercise Test and the Analysis of the Results of Coronary Diseases and Their Clinical Significance
Bibliographic record
Abstract
Objective In order to diagnose coronary heart and artery diseases more accurately,and to do exercise test and coronary angiography (CAG) on patients and suspect patients of coronary diseases so as to evaluate the relationship between them.Methods Choose 100 positive patients of exercise test and compare them with the cases of coronary angiography (CAG)so as to raise the rate of conformity in clinical diagnoses.Results With typical angina pectoris,there's a higher conformity rate of positive treadmill exercise test (TET) and positive coronary angiography (CAG).Without typical angina pectoris and dangerous factors of coronary heart diseases,the conformity rate of treadmill exercise test (TET) and positive coronary angiography (CAG) is lower.In positive exercise test,ST segment depresses in horizontal or in slope.The positive rate of coronary angiography (CAG) is high.If there are diseases in some branches of coronary artery,the ST segment depression time becomes long and there are more leads.Both are more than in the case of one branch of coronary artery diseases.Consider patients with high blood pressure or diabetes,the depression time of ST segment in exercise tests comes early and it lasts olng.This is completely different from that of pure coronary artery diseases.Conclusion The positive patients of treadmill exercise test (TET) are closely related to diseases of coronary artery.And it has higher sensitivity and specificity in diagnosing coronary artery diseases.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".