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Glucose metabolism in five septic neonatal foals

2008· article· en· W2146340898 on OpenAlexaff
Eduard Jose‐Cunilleras, Kenneth W. Hinchcliff, Yvette S. Nout, Raymond J. Geor

Bibliographic record

VenueJournal of Veterinary Emergency and Critical Care · 2008
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMedicineSepsisGlucagonCarbohydrate metabolismEndocrinologyInternal medicineInsulinMetabolismBacteremiaBiologyBiochemistry

Abstract

fetched live from OpenAlex

Abstract Objective: Glucose metabolism is often deranged in septic animals. Bacteremia and sepsis are common in foals and clinical experience suggests that glucose metabolism is abnormal in some of these animals. The purpose of this study was to provide initial estimates of rates of glucose appearance, disappearance, and metabolic clearance rate in septic foals. Series Summary: Rates of glucose entry, and exit from blood were determined by use of infusion of isotopically labeled glucose in 5 foals with confirmed sepsis. Serum concentrations of glucose, insulin, glucagon, and cortisol were measured concurrent with measurement of rates of glucose turnover. Median glucose turnover rate was 24 μmol/kg/min (range 17–53 μmol/kg/min), and median glucose metabolic clearance rate was 3.2 mL/kg/min (range 1.7–6.7 mL/kg/min). Median concentration of serum immunoreactive insulin was 55 pmol/L (range 36–190 pmol/L), median serum immunoreactive glucagon was 65 pmol/L (range 19–120 pmol/L), and median serum cortisol was 207 nmol/L (range 100–333 nmol/L). New or unique information provided: These data, although limited in scope and by the lack of data in healthy foals, demonstrate the magnitude and variation in glucose appearance, disappearance, and metabolic clearance rate in septic foals, provide an estimate of rates of glucose utilization in sick foals, and will be useful in guiding future studies of energy metabolism in healthy and ill foals.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.107
GPT teacher head0.415
Teacher spread0.308 · 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.

Study designCase report
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

Citations2
Published2008
Admission routes1
Has abstractyes

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