Serial blood lactate concentrations in systemically ill dogs
Bibliographic record
Abstract
BACKGROUND: Lactate concentration often is quantified in systemically ill dogs and interpreted based on human data. To our knowledge, there are no published clinical studies evaluating serial lactate concentrations as a prognostic indicator in ill dogs. OBJECTIVES: Our objective was to perform a prospective study, using multivariate analysis, to determine whether serial lactate concentrations were associated with outcome in ill dogs requiring intravenous fluids. METHODS: Eighty sick dogs had lactate concentrations evaluated, using an analyzer that measures lactate in the plasma fraction of heparinized whole blood, at 0 hours and 6 hours after initiation of treatment. Severity of illness and outcome (survivor, nonsurvivor) were determined by reviewing the patient's record 2 weeks after admission. Lactate concentrations, age, body weight, gender, and severity of illness were evaluated using multivariate analysis to determine their effects on outcome. RESULTS: Dogs with lactate concentrations greater than the reference interval at 6 hours were 16 times (95% confidence interval = 2.32-112.71 times, P <.01) more likely not to survive compared to dogs with lactate concentrations within the reference interval. Lactate concentrations above the reference interval at 0 hours were not significantly related to outcome. However, hyperlactatemia that did not improve by > or = 50% within 6 hours was significantly associated with mortality (P = .024). CONCLUSION: Dogs with a lactate concentration higher than the reference interval at 6 hours were more likely not to survive. These results indicate an association between lactate concentration and outcome and emphasize the importance of serial lactate concentrations in evaluating prognosis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".