Hydration of exercised Standardbred racehorses assessed noninvasively using multi‐frequency bioelectrical impedance analysis
Bibliographic record
Abstract
REASONS FOR PERFORMING STUDY: In human and animal clinical practice, multi-frequency bioelectrical impedance analysis (MF-BIA) is increasingly used as a diagnostic tool to assess hydration of intra-and extracellular fluid compartments. Accurate determination of changes in hydration status within individuals over time has remained problematic due to the requirement for complete impedance-frequency relationships at the time points of interest. OBJECTIVES: To use MF-BIA in 13 Standardbred racehorses and 7 'endurance' research horses to determine if MF-BIA could be used to track changes in total body water (TBW), intracellular fluid volume (ICFV) and extracellular fluid volume (ECFV) resulting from exercise. METHODS: Jugular venous blood was sampled at rest and for 2-13 h following exercise. TBW, ECFV and plasma volume (PV) were measured at rest using indicator dilution techniques (D2O, thiocyanate and Evans Blue, respectively). TBW, ECFV, ICFV and PV were correlated to impedance measures and predictive equations used to determine hydration status from MF-BIA measures. RESULTS: TBW loss continued throughout the recovery period, and was primarily borne by the ECF compartment at 90 min of recovery. CONCLUSIONS: MF-BIA predictions of compartmental hydration status were significantly correlated to measured/calculated decreases in these compartments. POTENTIAL RELEVANCE: Practical applications for MF-BIA in horses include monitoring of hydration status during transport and competition, assessment of body compostion, clinical health assessment and critical care management.
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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".