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Hypocalcemia and Hypovitaminosis D in Dogs with Induced Endotoxemia

2012· article· en· W1967474041 on OpenAlexaff
Marie K. Holowaychuk, Adam J. Birkenheuer, Jiaming Li, Henry S. Marr, A.P.M. Boll, Shila K. Nordone

Bibliographic record

VenueJournal of Veterinary Internal Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMedicineHypovitaminosisVitamin D and neurologyImmunologyInternal medicinevitamin D deficiency

Abstract

fetched live from OpenAlex

BACKGROUND: Hypocalcemia is a documented electrolyte disturbance in people and animals with sepsis, but its mechanism is poorly understood. OBJECTIVE: To investigate mechanisms of hypocalcemia in dogs with experimentally induced endotoxemia. ANIMALS: Six healthy mixed breed dogs were included in this nonrandomized, placebo-controlled, crossover study. METHODS: Dogs initially were injected with placebo (0.9% NaCl; 1 mL, IV) and then lipopolysaccharide (LPS; 2 μg/kg, IV) after a 5-day washout period. Blood and urine samples were collected for measurement of serum total calcium (tCa), ionized calcium (iCa), total magnesium (tMg), ionized magnesium (iMg), parathyroid hormone (PTH), 25-hydroxyvitamin D (vitamin D), venous blood gases, and fractional excretion (FE) of calcium. RESULTS: After LPS administration, body temperature increased and blood pressure decreased. Both iCa and tCa decreased (P < .01), but iMg was not significantly different between control and LPS treatments. PTH concentrations increased (P < .01) and vitamin D concentrations decreased (P < .01). Venous pH, bicarbonate, base excess, and blood glucose also decreased (P < .01). Urine tCa concentration was below the limit of detection for all dogs after LPS administration. CONCLUSIONS: Hypocalcemia occurs during endotoxemia in dogs and is associated with hypovitaminosis D. Hypomagnesemia, hypoparathyroidism, alkalosis, and increased calciuresis are not associated with hypocalcemia in endotoxemic dogs.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.046
GPT teacher head0.334
Teacher spread0.288 · 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 designObservational
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

Citations41
Published2012
Admission routes1
Has abstractyes

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