Validation of the handheld Lactate‐Pro analyzer for measurement of blood L‐lactate concentration in cattle
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Bibliographic record
Abstract
BACKGROUND: Blood L-lactate concentration (LAC) can be used for various diagnostic purposes in cattle. As multiple handheld analyzers for LAC exist, it is important to validate their use in cattle in comparison with reference laboratory blood analyzers. OBJECTIVES: The objectives of this study were to validate the handheld Lactate Pro meter (LacP) including reproducibility, and compare the measurements with the StatProfile (StatP) as a gold standard. In addition, diagnostic sensitivity and specificity, and the impact of HCT on LAC measured by both analyzers were assessed. METHODS: A cohort of 64 cattle with acute medical and surgical conditions was studied. Whole blood samples in heparin lithium tubes were analyzed upon arrival with both StatP and LacP. Twenty-three samples were immediately retested to assess intra-assay coefficient of variation (CV). The HCT values were also recorded. RESULTS: The LAC using LacP was highly correlated with the StatP (r = 0.9736 [95% confidence interval [CI]: 0.9562-0.9841]). The LacP underestimated LAC (mean difference:-0.9 mmol/L, 95% CI:-3.1 mmol/L to 1.3 mmol/L). The intra-assay CV was excellent (4.77%). No significant correlation was observed between LacP or StatP and HCT (P = .39 and .09, respectively). Sensitivity and specificity for LacP were 91.7% (95% CI: 76.4-97.8%) and 100% (83.4-100%, cutoff of 4 mmol/L), and 78.6% (58.5-90.9%) and 100% (87.0-100%, cutoff of 6 mmol/L). CONCLUSIONS: The LacP handheld lactate meter can be used safely and reliably cow-side, although it underestimates LAC value when compared with a standard laboratory analyzer especially for LAC ≥ 10.0 mmol/L. The LAC value was not influenced by HCT in this study.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 it