Longitudinal and radial systolic myocardial tissue velocities after prolonged exercise
Bibliographic record
Abstract
We assessed segmental and global left ventricular (LV) systolic function using tissue Doppler imaging (TDI) in 30 subjects (age: 18-62 y) before and after a marathon race. Longitudinal plane systolic (S') TDI velocities were assessed at 5 sites on the mitral annulus and radial plane S' velocities were assessed at the LV septal and free wall in a subsample (n = 9). Heart rate (HR) and LV diastolic internal dimension were also assessed before (pre) and immediately after (post) the race. Pre-post changes in all variables were analysed by repeated measures analysis of variance (ANOVA). Delta scores for TDI data were correlated with alterations in indices of LV loading, as well as with age and finishing time. Segmental longitudinal and radial TDI velocities were not significantly different pre to post race (p > 0.05), which resulted in no change in mean S' velocities (longitudinal: pre 17.0 +/- 3.4 cm x s(-1), post 17.4 +/- 4.0 cm x s(-)1; radial: pre 13.0 +/- 5.4 cm x s(-1), post 14.2 +/- 7.1 cm x s(-1); p > 0.05). Any pre-post changes in TDI data were not related to an elevated post race HR (r = 0.15, p > 0.05), a decreased post race LV internal dimension in diastole (r = 0.10, p > 0.05), age (r = -0.25, p > 0.05), or finishing time (r = -0.13, p > 0.05). Our data suggest that marathon running does not induce any segmental or global depression in LV systolic function.
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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".