Remote ischaemic preconditioning shortens QT intervals during exercise in healthy subjects
Bibliographic record
Abstract
UNLABELLED: The protective action of remote ischaemic preconditioning (RIPC) has been demonstrated in the context of surgical interventions in cardiology. Application of RIPC to sports performance has been proposed, but its effect on the electrocardiogram (ECG) during exercise remains unknown. This exploratory study aims to measure the changes in ventricular repolarization observed during exercise following RIPC in healthy subjects. In an experimental randomized crossover study, 17 subjects underwent two bouts of constant load exercise tests at 75% and 115% of gas exchange threshold (GET). Prior to exercise, they were allocated to either control or RIPC intervention with four cycles of 5 min of ischaemia followed by 5 min of reperfusion. ECG was continuously recorded during the protocol. QT and RR intervals were measured every 30 s (on an average tracing of the preceding 10 s). Although the time course of RR intervals did not differ between the two interventions (p = .56 at 75% GET and p = .74 at 115% GET), a significant shortening of QT intervals (measured from Q onset to T end) was observed during exercise (mean ± standard deviation of RIPC vs. CONTROL: -32 ± 19 ms at 75% GET (p < .001) and -34 ± 12 ms at 115% GET (p < .001)) as well as during recovery (-21 ± 8 ms at 75% GET (p < .001) and -16 ± 11 ms at 115% GET (p < .001)). This effect was not present at rest. These RIPC-related changes were clearly identifiable on the QT-RR loops after hysteresis reduction. RIPC therefore induces heart rate-independent shortening of QT intervals that is revealed during exercise.
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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".