Comparison of Three Point-of-Care Analyzers for the Measurement of Lactate Concentration in Chelonians
Bibliographic record
Abstract
Abstract Blood lactate concentration has been identified as a prognostic indicator in chelonians. However, comparison among various studies about lactate concentration is hindered by the fact that various point-of-care analyzers are used. This study evaluated agreement, correlation and biases between three commonly used analyzers, the Lactate Plus (Nova), i-STAT (Abbott), and EPOC (Heska). Forty chelonians of 10 different species were included in the study. The animals were recruited from a rehabilitation center and from the clientele of a veterinary teaching hospital, and presented with a variety of health statuses and a wide range of lactate concentration. A single blood sample obtained from each individual was placed in a heparinized tube and analyzed concomitantly by two analyzers per sample (20 chelonians for each analyzer). Agreement between the lactate and blood analyzers was evaluated via a Bland–Altman plot and correlation via a Spearman correlation coefficient. Bias was determined using a Passing–Bablok regression analysis. There was a fair agreement between the three techniques, but agreement was decreased for lactate concentrations above 5 mmol/L. Both blood analyzers reported consistently greater lactate concentrations compared to values obtained with the Lactate Plus, with a positive proportional bias. Correlations between the Lactate Plus and the two other point-of-care analyzers were excellent, 97.0% and 92.8% with the i-STAT and the EPOC analyzers, respectively. This study highlights the need to conduct studies in various reptile species to evaluate the performance of point-of-care analyzers.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".