Tonicity of oral rehydration solutions affects water, mineral and acid–base balance in calves with naturally occurring diarrhoea
Bibliographic record
Abstract
Abstract Recommendations for composition of oral rehydration solutions (ORS) for calves, particularly concerning Na+, glucose, and their combined effect on tonicity, are not in line with guidelines for humans. Thus, this study aimed to determine the effect of ORS tonicity on water, mineral and acid–base balance. Seventy‐two calves were selected based on the severity of dehydration and blood base excess (BE) on day 0. Five calves that did not develop diarrhoea were removed post‐inclusion from the study. Calves were allocated to blocks of four animals based on blood BE on day 1. Within each block, calves were randomly assigned to one of four treatments: (a) hypotonic ORS with low Na+ and lactose (HYPO); (b) isotonic ORS with low Na+and glucose (ISO); (c) hypertonic ORS with high Na+ and glucose (HYPER); and (d) control consisting of warm water including 5 g/L of whey powder (CON). Treatments were administered twice daily over a 3‐day period, in which calves were offered 2.0 L of treatment at 1300 and 2100 hr. Calves were fed 2.5 L of milk replacer at 0700 and 1630 hr from day 1 to 3 and 3.0 L from day 4 to 5, and had access to water. Calves were monitored for 5 days in which measurements included intakes, BW, blood sampling and collection of faeces on day 1 and urine from day 1 to 3. All ORS treatments maintained normal serum Na+, whereas CON did not. Calves in the HYPER group had lower blood pH and greater faecal Na+ losses than HYPO and ISO. Plasma expansion relative to baseline was higher in low tonicity ORS (+4.8%) when compared with CON (+1.0%). Urine osmolality was 30% higher in HYPER calves. In this experiment, low tonicity ORS were more effective at restoring water, mineral and acid–base balance than the hypertonic ORS.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".