Determining dehydration and its compartmentation in horses at rest and with exercise: a concise review and focus on multi-frequency bioelectrical impedance analysis
Bibliographic record
Abstract
Multi-frequency bioelectrical impedance analysis (MFBIA) has been, and likely will increasingly be, used to rapidly and non-invasively assess the time course of volume losses and recovery in horses. Dehydration in performance horses is frequently the cause of health and performance problems, and presently used techniques for objectively quantifying optimum hydration are time consuming and challenging to perform accurately. Dehydration can take a number of different forms, with a balanced loss of water and electrolytes from both extra- and intracellular fluid compartments, or a primarily extracellular or intracellular dehydration. This review summarises the current state of knowledge regarding the quantification of dehydration, losses of water and electrolytes from extra- and intracellular fluid compartments. The effects of dehydration on exercise performance, muscle function, cardiovascular function, thermoregulation and feeding are briefly summarised. The review provides a quantitative description of the magnitude and time course of compartmental fluid losses and recovery in horses in response to feeding and due to exercise at different intensities and durations representing the endurance horse to the track race horse. Effective rehydration requires knowledge of the losses from the main body fluid compartments, which is now possible using MFBIA technology. The present review outlines the key approaches that have been used to assess dehydration in horses, including the new technique of MFBIA.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".