Serum D-Lactate Concentrations in Cats with Gastrointestinal Disease
Bibliographic record
Abstract
BACKGROUND: Increased D-lactate concentrations cause neurological signs in humans with gastrointestinal disease. HYPOTHESIS/OBJECTIVES: To determine if serum D-lactate concentrations are increased in cats with gastrointestinal disease compared to healthy controls, and if concentrations correlate with specific neurological or gastrointestinal abnormalities. ANIMALS: Systematically selected serum samples submitted to the Gastrointestinal Laboratory at Texas A&M University from 100 cats with clinical signs of gastrointestinal disease and abnormal gastrointestinal function tests, and 30 healthy cats. METHODS: Case-control study in which serum D- and L-lactate concentrations and retrospective data on clinical signs were compared between 30 healthy cats and 100 cats with gastrointestinal disease. Association of D-lactate concentration with tests of GI dysfunction and neurological signs was evaluated by multivariate linear and logistic regression analyses, respectively. RESULTS: All 100 cats had a history of abnormal gastrointestinal signs and abnormal gastrointestinal function test results. Thirty-one cats had definitive or subjective neurological abnormalities. D-lactate concentrations of cats with gastrointestinal disease (median 0.36, range 0.04-8.33 mmol/L) were significantly higher than those in healthy controls (median 0.22, range 0.04-0.87 mmol/L; P = .022). L-lactate concentrations were not significantly different between the 2 groups of cats with gastrointestinal disease and healthy controls. D-lactate concentrations were not significantly associated with fPLI, fTLI, cobalamin, folate, or neurological abnormalities (P > .05). CONCLUSIONS AND CLINICAL IMPORTANCE: D-lactate concentrations can be increased in cats with gastrointestinal disease. These findings warrant additional investigations into the role of intestinal microbiota derangements in cats with gastrointestinal disease, and the association of D-lactate and neurological abnormalities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".