Exercise-Induced Ischemic Preconditioning and the Potential Application to Cardiac Rehabilitation
Bibliographic record
Abstract
In Brief Exercise-induced ischemic preconditioning (IPC) can be assessed by the results of the second of sequential exercise tests. Exercise-induced IPC is quantified by using the time to 1-mm ST-segment depression, the rate-pressure product at 1-mm ST-segment depression, the maximal ST-segment depression, and the rate-pressure product at the peak of exercise. Few studies reported whether exercise-induced IPC could be used in cardiovascular rehabilitation. A systematic review of the literature limited to human studies was performed using electronic databases, and the main key words were ischemic preconditioning, warm-up phenomenon, and exercise. After careful review, 38 articles were included in the systematic review. This review summarizes the molecular pathways of IPC and describes the first window of protection induced by sequential exercise tests, as well as the effect of medication on exercise-induced IPC. A section on the exercise protocol, mode of exercise, and intensity provides understanding as to what is needed for clinicians to induce IPC with sequential stress tests. The final section of the review is a discussion of the potential use of exercise-induced IPC in a cardiovascular rehabilitation setting. Even if exercise-induced IPC is a well-documented phenomenon, additional studies are needed in order to more fully understand its use in rehabilitation. Exercise-induced ischemic preconditioning can be assessed using the results of the second exercise stress test of sequential testing. Thirty-eight articles were included in the review. The review mainly describes the first window of protection induced by sequential exercise stress test as well as the effect of medication on exercise-induced ischemic preconditioning.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".