Validation of the handheld Lactate‐Pro analyzer for measurement of blood L‐lactate concentration in cattle
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".