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Record W4396640275 · doi:10.1111/vcp.13350

Spurious hyperbicarbonatemia and a negative anion gap in a cat and a dog with severe rhabdomyolysis

2024· article· en· W4396640275 on OpenAlexaboutno aff
Jennifer Bouschor, Christopher S. Shiprack, Zachary Lake, Molly A. Racette, Daniel A. Heinrich

Bibliographic record

VenueVeterinary Clinical Pathology · 2024
Typearticle
Languageen
FieldMedicine
TopicMuscle and Compartmental Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsAnion gapBicarbonateCATSLactate dehydrogenaseVenous bloodRhabdomyolysisMedicineChemistryInternal medicineEndocrinologyBiochemistryEnzymeMetabolic acidosis

Abstract

fetched live from OpenAlex

A 3-year-old male neutered domestic shorthair cat and a 2-year-old male neutered Labrador-mix dog were separately presented to the Veterinary Medical Center for evaluation after sustaining significant muscle trauma due to a dog attack and seizure activity, respectively. In both cases, biochemical analysis was consistent with rhabdomyolysis. Additionally, a markedly increased measured serum bicarbonate concentration and negative calculated anion gap were observed. As these biochemical abnormalities were not expected and deemed incompatible with life, an interference with the analyzer measurement of bicarbonate involving marked increases in pyruvate and lactate dehydrogenase (LDH) following myocyte injury was suspected. Venous blood gas analysis calculated bicarbonate concentration and anion gap were within reference interval, while measured LDH activity was markedly increased. These findings supported an analyzer-generated interference. This is the first published report of a previously described chemistry analyzer interference of markedly increased LDH activity with serum bicarbonate concentration measurement in dogs and cats. Awareness of this interference is important, particularly in the emergency setting, as it may influence case management.

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 score0.255
Threshold uncertainty score0.453

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 head0.055
GPT teacher head0.370
Teacher spread0.315 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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