Evaluation of a point-of-care meter for measuring glucose concentrations in dairy calves: A diagnostic accuracy study
Bibliographic record
Abstract
The objective of this cross-sectional, diagnostic accuracy study was to validate a human blood glucose meter (Contour Next One Meter, Ascensia Diabetes Care) for accuracy and precision when measuring blood glucose, and for diagnostic accuracy for hypoglycemic status in dairy calves using whole blood and blood plasma. A total of 49 male dairy calves [body weight (BW): 46.3 ± 0.8 kg] had jugular catheters placed within 75 min after birth. Thereafter, blood was withdrawn from the catheter at specific time points (−10, 10, 20, 30, 45, 60, 90, 120, 180, 240, 360, 480, and 600 min) relative to the first and second colostrum feedings (2 h 15 min and 14 h 5 min postnatal; feeding rate: 7% of BW wt/wt). The reference standard method for plasma glucose concentration was determined colorimetrically and in duplicate using the glucose oxidase-peroxidase reaction. Data were assessed for agreement between the glucose meter and the reference standard using Lin's concordance correlation coefficients (CCC), coefficients of determination (precision), and Bland-Altman plots. In addition, a mixed linear regression model was built using the reference method as the outcome, with the glucose meter and repeated measures of time as the explanatory variables and calf as a random effect. The sensitivity (Se), specificity (Sp), and area under the curve (AUC) for the glucose meter using calf whole-blood and plasma were calculated at a threshold of <4.44 mmol/L to determine hypoglycemia. The precision (CCC = 0.95, R 2 = 0.93) and accuracy (AUC = 0.98) of the glucose meter were very high when used on 1,303 blood plasma samples. Youden's index revealed a threshold of <4.45 mmol/L for the glucose meter when used with plasma, leading to Se of 94.2% and Sp of 91.9%, with 92.5% of samples being correctly classified, suggesting high diagnostic accuracy. When using whole blood, precision (CCC = 0.85 and R 2 = 0.73) and accuracy (AUC = 0.92) were high when used on 476 samples. Youden's index revealed a threshold of <4.95 mmol/L for the glucose meter when used with whole calf blood, leading to Se of 95.6% and Sp of 80.3%, with 84.7% of samples being correctly classified, suggesting high diagnostic accuracy for use on farm. In summary, this glucose meter was validated for measuring calf blood glucose using both plasma and whole blood. This meter can measure glycemic status in calves and may be useful for clinical and on-farm use to make intervention decisions.
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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 | 0.022 | 0.040 |
| 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 | 0.001 |
| 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".