Individual variability in cardiac biomarker release after 30 min of high-intensity rowing in elite and amateur athletes
Bibliographic record
Abstract
This study had two objectives: (i) to examine individual variation in the pattern of cardiac troponin I (cTnI) and N-terminal pro-brain natriuretic peptide (NT-proBNP) release in response to high-intensity rowing exercise, and (ii) to establish whether individual heterogeneity in biomarker appearance was influenced by athletic status (elite vs. amateur). We examined cTnI and NT-proBNP in 18 elite and 14 amateur rowers before and 5 min, 1, 3, 6, 12, and 24 h after a 30-min maximal rowing test. Compared with pre-exercise levels, peak postexercise cTnI (pre: 0.014 ± 0.030 μg·L(-1); peak post: 0.058 ± 0.091 μg·L(-1); p = 0.000) and NT-proBNP (pre: 15 ± 11 ng·L(-1); peak post: 31 ± 19 ng·L(-1); p = 0.000) were elevated. Substantial individual heterogeneity in peak and time-course data was noted for cTnI. Peak cTnI exceeded the upper reference limit (URL) in 9 elite and 3 amateur rowers. No rower exceeded the URL for NT-proBNP. Elite rowers had higher baseline (0.019 ± 0.038 vs. 0.008 ± 0.015 μg·L(-1); p = 0.003) and peak postexercise cTnI (0.080 ± 0.115 vs. 0.030 ± 0.029 μg·L(-1); p = 0.022) than amateur rowers, but the change with exercise was similar between groups. There were no significant differences in baseline and peak postexercise NT-proBNP between groups. In summary, marked individuality in the cTnI response to a short but high-intensity rowing bout was observed. Athletic status did not seem to affect the change in cardiac biomarkers in response to high-intensity 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 | 0.001 | 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".