MétaCan
Menu
Back to cohort

Effects of Prednisone on Blood Lactate Concentrations in Healthy Dogs

2009· article· en· W2080354002 on OpenAlexaff
Søren Boysen, Mary Clarisse Bozzetti, Lara Rose, Marilyn Dunn, Daniel Pang

Bibliographic record

VenueJournal of Veterinary Internal Medicine · 2009
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsCegep de Saint HyacintheUniversité de MontréalUniversity of Calgary
Fundersnot available
KeywordsHyperlactatemiaMedicinePrednisoneBeagleBlood lactateInternal medicineLactic acidBlood pressureHeart rateBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Glucocorticoids affect carbohydrate and lactate metabolism. HYPOTHESIS: Administration of prednisone to healthy dogs will result in clinically relevant hyperlactatemia. ANIMALS: Twelve healthy adult Beagle dogs. METHODS: Prospective, controlled experimental study. Twelve healthy adult Beagles were divided into 2 groups (3 of each sex per group). One group served as control. The other group received 2 treatments: low, 1 mg/kg prednisone PO q24h for 2 weeks; high, 4 mg/kg prednisone PO q24h for 2 weeks. A washout period of 6 weeks separated the treatments. Blood samples were drawn for whole blood lactate measurement on day (D) 0, D4, and D14 and measured in duplicate. RESULTS: Compared with the control group, low and high groups had significantly higher blood lactate concentrations at D4 and D14. There was no difference at D0. There was no effect of time within the control group. In the low and high groups, blood lactate concentration was increased at D4 and D14 versus D0. Blood lactate concentration was greater in the high group than the low group at D14 only. CONCLUSIONS AND CLINICAL IMPORTANCE: Dogs treated with prednisone experience statistically significant increases in blood lactate concentrations, which can result in type B hyperlactatemia. In such cases, improving tissue perfusion, treatment for the commonest form of hyperlactatemia (type A) would be unnecessary.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.823
Threshold uncertainty score0.923

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.053
GPT teacher head0.367
Teacher spread0.314 · 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

Citations26
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Veterinary Internal MedicineSame topicVeterinary Medicine and SurgeryFrench-language works237,207