ENHANCED EXTERNAL COUNTERPULSATION EFFECTIVENESS ON CLINICAL PARAMETERS IN DIABETIC AND NON-DIABETIC CORONARY HEART DISEASE PATIENTS
Bibliographic record
Abstract
Objectives: The objectives of the study were to assess the effectiveness of enhanced external counterpulsation (EECP) treatment on clinical profile comprising physiological, biochemical, and clinical symptoms of diabetic and non-diabetic coronary heart disease (CHD) patients.Methods: A pretest–posttest designed prospective study with 163 diabetic and non-diabetic CHD patients enrolled in Science and Art of Living Heart Center (SAAOL), New Delhi, India. Angina severity was assessed using Canadian Cardiovascular Society (CCS) angina classification scale and dyspnea status was assessed using medical research council (MRC) scale. The study subjects were followed up for 12 months. Statistical analysis was done using the SPSS v21 software. Descriptive analysis with sample t-test for two independent groups and paired sample t-test for EECP effectiveness within the group was done.Results: A minute difference in body mass index mean (30.1±5.86–29.9±5.62 vs. 27.5±4.17–27.16±3.88) was observed in diabetic and non-diabetic CHD patients, but that was not statistically significant. A significant drop out in blood sugar fasting (166.7±41.9–150.1±23.7), blood sugar postprandial (204.7±64.4–173.2±41.2), and glycosylated hemoglobin (7.9±0.8 to 7.5±0.6) was also observed in diabetic CHD patients from baseline to 12th month after completion of EECP treatment with significant p<0.001, that may be due to EECP treatment. CCS angina classification score and MRC dyspnea score also significantly improved after EECP treatment.Conclusion: EECP treatment may improve clinical symptoms of CHD and lower the blood glucose level in diabetic CHD patients. This treatment may be effective for CHD patients with diabetes mellitus.
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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 | 0.001 |
| 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".