Intradialytic exercise preconditioning: an exploratory study on the effect on myocardial stunning
Bibliographic record
Abstract
BACKGROUND: Exercise preconditioning provides immediate protection against cardiac ischemia in clinical/preclinical studies in subjects without chronic kidney disease. In individuals requiring renal replacement therapy, hemodialysis (HD) results in significant circulatory stress, causing acute ischemia with resultant recurrent and cumulative cardiac injury (myocardial stunning). Intradialytic exercise (IDE) has been utilized to improve functional status in individuals receiving HD. The objective of this study was to explore the role of IDE as a preconditioning intervention and assess its effect on HD-induced myocardial stunning. METHODS: We performed a single-center cross-sectional exploratory study in adults on chronic HD participating in a clinical IDE program. HD-induced cardiac stunning was evaluated over two HD sessions within the same week: a control visit (no exercise) and an exposure visit (usual intradialytic cycling). Echocardiography was performed at the same three time points for each visit. Longitudinal strain values for 12 left ventricular segments were generated using speckle-tracking software to assess the presence of HD-induced regional wall motion abnormalities (RWMAs), defined as a ≥20% reduction in strain; two or more RWMAs represent myocardial stunning. RESULTS: A total of 19 patients were analyzed (mean age 57.2 ± 11.8 years, median dialysis vintage 3.8 years). The mean number of RWMAs during the control visit was 4.5 ± 2.6, falling to 3.6 ± 2.7 when incorporating IDE (a reduction of -0.95 ± 2.9; P = 0.17). At peak HD stress, the mean number of RWMAs was 5.8 ± 2.7 in the control visit versus 4.0 ± 1.8 during the exposure visit (a reduction of -1.8 ± 2.8; P = 0.01). CONCLUSION: We demonstrated for the first time that IDE is associated with a significant reduction in HD-induced acute cardiac injury.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".