Admission lactate concentration has predictive value for death or severe complications within 30 days after admission in cattle with long-bone fractures
Bibliographic record
Abstract
OBJECTIVE: To determine the prognostic value of lactate concentration measurements at admission in cattle with long-bone fractures. ANIMALS: 43 cattle with long-bone fractures between July 2016 and Dec 2018. PROCEDURES: In this prospective cohort study, lactate concentration was measured in systemic venous blood and locally in capillary blood sampled from the fractured and contralateral limbs of cattle and assessed for outcome prediction. The cutoff value was determined by maximizing the Youden index from receiver-operating characteristic curves. Multivariable logistic regression was employed to verify whether higher lactate concentrations compared with the cutoff value were an independent risk factor for poor outcomes at 30 days or 3 years after admission. RESULTS: Poor outcome was associated with higher capillary lactate concentration in the fractured limb (P < .001) and greater difference with systemic blood (P = .005). A cutoff value of lactate difference ≥ 2.4 mmol/L (sensitivity = 0.80; specificity = 0.965) between capillary lactate in the fractured limb and systemic blood was the best predictor of death ≤ 30 days after admission (P < .001). Multivariable analysis revealed that this cutoff value was an independent risk factor for 30-day and 3-year outcomes (P < .001). CLINICAL RELEVANCE: Admission blood lactate concentration difference ≥ 2.4 mmol/L between the fractured limb and systemic blood was a robust and independent predictor of outcome for cattle of the present report. Lactate metabolism is locally impaired in fractured limbs of nonsurviving or at higher complication risk cattle, which may help identify patients at high risk for poor outcomes.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".