Heart Conditioning as a Healthy Strategy in Management of Cardiac Enlargement
Bibliographic record
Abstract
Background: Remote ischemic conditioning (RIC) is widely recognized for its cardioprotective effects in the context of ischemic heart disease. Lately, it has been shown that heart conditioning can be utilized as a healthy strategy in the reversion of disease and aging. In this regard, we examine the impact of RIC on patients with cardiac enlargement. Methods: Forty-four patients with cardiac enlargement were prospectively enrolled and randomly assigned into RIC group (n = 22) and control group (n = 22). RIC protocol is 3-min inflation/deflation of the blood pressure cuff applied in the upper arm to create transient arm ischemia. RIC treatment was performed once a day for 1 year. Left atrial and ventricular dimensions and left ventricular ejection fraction (LVEF) were all assessed in two groups. Results: RIC was well-tolerated. After 1 year treatment, left atrial and ventricular dimensions were significantly decreased in the RIC group. Moreover, LVEF showed a significant increase, from 46.24% to 56.45% (P < 0.0001). Conclusion: The research indicates that a year-long healthy regimen of RIC treatment may effectively reverse cardiac enlargement, thereby endorsing the broader implementation of RIC in the daily routines of these patients.
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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".