Prediction of hydration status using multi-frequency bioelectrical impedance analysis during exercise and recovery in horses
Bibliographic record
Abstract
The present study tested the hypothesis that multi-frequency bioelectrical impedance analysis (MFBIA) can be used to provide reasonable estimates of body mass, total body water (TBW), extracellular fluid volume (ECFV) and plasma volume (PV) at rest, during exercise-induced dehydration and subsequent recovery. Seven exerciseconditioned horses were administered indicators for measurement of resting TBW, ECFV and PV. MFBIA measurements at 24 frequencies between 5 and 280 kHz were obtained at rest, during prolonged submaximal exercise and for up to 13 h of recovery with food and water provided. Impedance ‐frequency response curves were described by a double-exponential decay equation from which coefficients were used, together with height and length, to generate predictive equations for estimating body mass, TBW, ECFV and PV. Predictive equations for body mass, ECFV and PV provided reasonable estimates of the parameter at rest and during exercise and recovery that were within 6% of absolute values determined using indicators. Despite the inherent error in estimating absolute volumes, the technique allowed accurate (within 1%) determination of the change in compartment volumes within individual horses over time. The number of frequencies at which impedance was measured could be reduced to seven without sacrificing the accuracy of the impedance‐frequency relationships or the predictive equations ‐ this enabled a 70% reduction in data-acquisition time (to , 35 s) for each MFBIA measurement series. It is concluded that MFBIA can be used in individual horses to track changes in compartmental hydration status resulting from dehydration and rehydration.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".