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Record W4414406473 · doi:10.1371/journal.pone.0333135

Volume kinetics of crystalloid and colloid solutions administered to healthy anesthetized cats

2025· article· en· W4414406473 on OpenAlexafffund
Xiu Ting Yiew, Robert G. Hahn, William W. Muir, Carolyn L. Kerr, Shane Bateman

Bibliographic record

VenuePLoS ONE · 2025
Typearticle
Languageen
FieldMedicine
TopicHemodynamic Monitoring and Therapy
Canadian institutionsUniversity of Guelph
FundersOVC Pet Trust
KeywordsCATSBolus (digestion)PharmacokineticsDilutionCrossover studyPopulationPlasma clearanceUrineBlood volume

Abstract

fetched live from OpenAlex

This prospective experimental study evaluated the disposition of a crystalloid and a colloid solution in 10 healthy cats under general anesthesia. Each cat was randomly assigned to receive either 20 mL/kg of a balanced isotonic crystalloid solution (PLA) or 5 mL/kg of 6% tetrastarch 130/0.4 solution (T-HES), administered over 15 minutes, in a 2-period, 2-treatment crossover design. Blood samples were collected, and urine output was measured during a 3-hour experimental period. Plasma dilution was calculated using serial hemoglobin concentrations and red blood cell count. Volume kinetics (distribution and elimination) of each fluid were determined using non-linear mixed effects pharmacokinetic modeling software. Data from a previous study with a similar methodology in healthy conscious cats were included in the population kinetic analysis, revealing anesthesia as a significant covariate for k21 (peripheral-to-central intercompartmental rate constant) for PLA and k10 (dilution-dependent first-order elimination rate constant) for T-HES. Cumulative urine output under general anesthesia was approximately 3.5 times lower for PLA and 2.5 times lower for T-HES compared to conscious cats. Overall, our data suggest that the elimination of PLA and T-HES is markedly reduced, and a bolus of PLA produces a short period of plasma expansion with the potential to cause significant peripheral fluid accumulation in cats during general anesthesia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.052
GPT teacher head0.289
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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