Cardiovascular Consequences of Completing a 160-km Ultramarathon
Bibliographic record
Abstract
PURPOSES: To comprehensively investigate the cardiovascular consequences of a 160-km ultramarathon using traditional echocardiography, speckle tracking imaging, cardiac biomarkers, and heart rate variability (HRV) and to examine the relationship between the changes in these variables. METHODS: We examined athletes before an ultramarathon and reassessed all finishers immediately after the race. Left ventricular (LV) systolic (ejection fraction [EF], systolic blood pressure/end-systolic volume [SBP/ESV] ratio) and diastolic (ratio of early [E] to late [A], filling E:A) measurements were assessed using traditional echocardiography, whereas myocardial peak strain and strain rate were analyzed using speckle tracking. Cardiac biomarkers measured were cardiac troponin T (cTnT) and N-terminal pro-brain natriuretic peptide (NT-pro-BNP). HRV indices were assessed using standard frequency and time domain measures. RESULTS: Twenty-five athletes successfully completed the race (25.5 +/- 3.2 h). Significant pre- to postrace changes in EF (66.8 +/- 3.8 to 61.2 +/- 4.0 %, P < 0.05) and E:A ratio (1.62 +/- 0.37 to 1.35 +/- 0.33, P < 0.05) were reported. Peak strain was significantly decreased in all planes, with the largest reduction occurring circumferentially. NT-pro-BNP concentrations increased significantly (28 +/- 17.1 vs 795 +/- 823 ng x L, P < 0.05), whereas postrace cTnT were elevated in just five athletes (20%). No significant alterations in HRV were noted postrace. Reductions in LV function were not significantly associated with changes in cardiac biomarkers and/or HRV. CONCLUSIONS: Although the stress of an ultramarathon resulted in a mild reduction in LV function and biomarker release, the mechanisms behind such consequences remain unknown. It is likely that factors other than myocardial damage or strong vagal reactivation contributed to postexercise decreases in LV function after an ultramarathon.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".