A consort-guided randomized, blinded, controlled clinical trial on the effects of 6 weeks training on heart rate variability in thoroughbred horses
Bibliographic record
Abstract
Abstract Background Fitness assessment of horses remains challenging. Heart rate variability (HRV) can be used to monitor human athlete's training, but its value is unknown in horses. Hypothesis The linear domain HRV variables are affected by fitness. Animals Twelve healthy untrained thoroughbreds were randomly split into a training group (6 weeks of incremental racetrack training) and a control group (no training). Methods Linear domain HRV variables were analyzed (high frequency [HF], low frequency [LF], their normalized units [Hfnu, Lfnu], root mean square of successive differences between beats [RMSSD], Poincaré plot features [SD1 and SD2]) while resting overnight before (baseline) and after 2, 4 and 6 weeks of training. V̇O2max and echocardiographic indexes were measured at baseline and after 6 weeks. Changes in HRV variables over time (ANOVA), and correlation with V̇O2max (Pearson's chi-squared test) were tested (P < .05 significance). Results V̇O2max, LF/HF ratio, and LFnu increased while HFnu decreased in the training group (before and after training mean [SD] values: V̇O2max 134 [12.8]-146 [16.5] mL/[kg min]; P < .001; LF/HF ratio 0.51 [0.2]-0.91 [0.3] [P = .02]; LFnu 37.5 [10.1]-46.8 [7.8] [P = .02]; HFnu 76.2 [7.9]-53.2 [7.7] [P < .001]). Training did not affect RMSSD, SD1, SD2, resting HR, or echocardiographic indexes. Strong correlations were found between V̇O2max and HRV variables (V̇O2max and LFnu [r = −0.59, P = .04]; ΔV̇O2max and the corresponding ΔLF [r = −0.88, P = .02]). Conclusions and Clinical Importance Six weeks of training affected some frequency domain HRV variables. Further studies are necessary to validate the use of HRV for monitoring horses' responses to training.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".