Prognostic Value of Cardiac Troponin I and L-Lactate in Blood of Dairy Cows Affected by Downer Cow Syndrome
Bibliographic record
Abstract
BACKGROUND: The downer cow syndrome (DCS) is a challenging health issue in the dairy industry. No cow-side test is available to provide an accurate prognosis for DCS cases in farm settings. HYPOTHESIS/OBJECTIVES: Local or systemic hypoperfusion and myocardial lesions lead to an increase in blood concentration of biomarkers cardiac troponin I (cTnI) and L-lactate. The objective was to determine the prognostic values of these biomarkers assessed cow-sides in addition to clinical examinations in prognostication of a negative outcome (NO: death or euthanasia within 7 days). ANIMALS: 218 client-owned dairy cows affected by DCS. METHODS: In a prospective study, animals were monitored for 60 days after inclusion of each cow. Blood cTnI and L-lactate concentrations were measured on the day of inclusion. The prognostic accuracy of both biomarkers and physical examination variables was estimated to predict NO. A mixed multivariable logistic regression model was used for data analysis. RESULTS: Prevalence of NO in this study was 63% on day 7. Troponin concentrations greater than 0.7 ng/mL had sensitivity and specificity of 54.1% (95% CI: 45.3-62.7%) and 78.4% (95% CI: 67.3-87.1%), respectively, for predicting NO. Blood L-lactate was not associated with the outcome. The multivariable model revealed that heart rate >100 bpm (OR; 95% CI: 3.7; 1.3-10.2) and cTnI > 0.7 ng/mL (OR; 95% CI: 5.5; 2.1-14.6) were associated with the risk of NO. CONCLUSIONS AND CLINICAL IMPORTANCE: Hypertroponinemia and tachycardia were associated with reduced survival in DCS cases. The use of cow-side blood cTnI concentrations and heart rate could help to rapidly identify cows in farm setting that have poor chances of recovery and would benefit from a more aggressive treatment or euthanasia.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".