Effect of three resuscitative fluid therapy strategies on NT‐proBNP concentration in healthy dogs
Bibliographic record
Abstract
OBJECTIVE: To determine if 3 resuscitative fluid therapy strategies increase N-terminal pro-brain natriuretic peptide (NT proBNP) levels in healthy dogs. DESIGN: Randomized crossover trial. SETTING: Veterinary teaching hospital. ANIMALS: Plasma NT proBNP concentrations were measured in 6 healthy purpose-bred dogs that received 3 resuscitative fluid therapy protocols. MEASUREMENTS AND MAIN RESULTS: Crystalloid, synthetic colloid, or hypertonic saline fluids were administered at resuscitative doses. Blood samples were collected via an indwelling catheter before, and at set time points between 0.5 and 36 h after fluid therapy and analyzed for NT-proBNP. A general linear mixed model was used to estimate the differences in NT-proBNP over time and among treatments. None of the resuscitative fluid therapy protocols caused increases of serum NT-proBNP beyond the previously reported cutoff concentration used to differentiate cardiac versus noncardiac causes of respiratory signs. Dogs receiving crystalloid fluid therapy had the most significant and prolonged increase in serum NT-proBNP concentration above baseline compared to dogs receiving either resuscitative doses of colloids or hypertonic saline. CONCLUSIONS: Serum NT-proBNP concentration in normal dogs was not increased beyond concentrations previously established to equate to cardiac disease after receiving resuscitative fluid therapy with either a balanced crystalloid solution, hypertonic saline, or a synthetic colloid solution in this study.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".