Evaluation of the Accutrend for lactate measurement in dogs
Bibliographic record
Abstract
BACKGROUND: Lactate concentrations are increasingly quantified in dogs using point-of-care instruments, but often without canine-specific method evaluation and instrument-specific reference intervals. OBJECTIVES: The objectives of this study were to 1) determine the precision of the Accutrend (Roche Diagnostics) for lactate determination in dogs, 2) determine the accuracy of the Accutrend using the Rapidlab 865 (Bayer Diagnostics) as the reference method, and 3) establish and compare reference intervals for lactate concentration in clinically healthy dogs for both instruments. METHODS: Precision was evaluated using low and high control materials, and variable (1 drop) and fixed (25 microL) sample volumes. Accuracy was determined by comparing lactate concentrations obtained with the Accutrend with those from the Rapidlab 865 in 273 heparinized canine jugular venous blood samples from 100 clinically healthy dogs and 107 systemically ill dogs (173 samples). Lactate reference intervals were established for both analyzers using data from the 100 clinically healthy dogs. RESULTS: The precision of the Accutrend was good (coefficients of variation, < or = 5.3%) for 25-microL samples but not when a drop was used. Lactate concentrations obtained on the Accutrend correlated poorly with those from the Rapidlab 865 (r = 0.864, mean bias = 0.66 mmol/L, 95% confidence interval [CI] = 0.57-0.76 with 95% limits of agreement = -0.87 (lower limit, 95% CI = -1.03 to -0.71) and 2.20 (upper limit, 95% CI = 2.04 to 2.36). The reference interval for canine lactate concentration on the Accutrend was 1.2-3.1 mmol/L compared with 0.46-2.31 mmol/L on the Rapidlab. CONCLUSION: Although precision was good with fixed sample volumes, blood lactate concentrations obtained on the Accutrend were significantly different than those on the Rapidlab 865, with systematic and random errors resulting in a positive bias. Further evaluation of the Accutrend is required before its use in dogs can be recommended.
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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 | 0.011 | 0.018 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".