Prospective Evaluation of Clinically Relevant Type B Hyperlactatemia in Dogs with Cancer
Bibliographic record
Abstract
BACKGROUND: Cancer is considered a cause of type B hyperlactatemia in dogs. However, studies evaluating cancer as a cause of clinically relevant type B hyperlactatemia (>2.5 mmol/L) are lacking. Cancer cells have a higher lactate production because of increased aerobic glycolysis, known as the “Warburg effect.” The mechanisms through which aerobic glycolysis occurs are not well elucidated, but neoplasia may cause type B hyperlactatemia via this process. OBJECTIVES: To determine if malignant tumors of dogs are associated with clinically relevant type B hyperlactatemia (>2.5 mmol/L). ANIMALS: Thirty-seven client-owned dogs with malignant tumors: 22 with hematopoietic and 15 with solid tumors. METHODS: Histology was used to confirm the diagnosis (cytology was considered adequate for diagnosis of lymphoma). Confounding conditions associated with hyperlactatemia were excluded. Lactate measurements were immediately performed on free-flow jugular whole blood samples using the LactatePro analyzer. RESULTS: All dogs had lactate concentrations<2.5 mmol/L. Mean blood lactate concentration was 1.09 mmol/L. Mean blood lactate concentrations for solid and hematopoietic tumors were 0.95 and 1.19 mmol/L, respectively. Dogs with lymphoma (n=18) had a mean blood lactate concentration of 1.15 mmol/L. CONCLUSIONS: Malignant tumors were not considered a cause of clinically relevant type B hyperlactatemia. Therefore, cancer-related type B hyperlactatemia in dogs is uncommon, and hyperlactatemia should prompt careful investigation for causes other than cancer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 source (direct Gemma or distilled Codex), 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".