Evaluation of the Freestyle Optium Neo H point-of-care device for measuring blood glucose concentrations in sick calves
Bibliographic record
Abstract
BACKGROUND: Data on the performance of a glucometer in calves with different diseases are currently lacking. OBJECTIVE: The primary objective of this study was to evaluate the reliability of a point of care glucometer in calves affected by different diseases relative to a traditional bench-top autoanalyzer. ANIMALS: One hundred ninety-six calves with different disorders in a referral hospital. METHODS: Prospective study. Venous blood samples were used for the determination of glucose concentrations in blood and plasma using the Freestyle Optium Neo H and autoanalyzer, respectively. Data were subjected to Passing-Bablok regression and Bland-Altman plots. The Freestyle Optium Neo H was the test method and the autoanalyzer was the reference method. The diagnostic performance of the glucometer relative to the autoanalyzer was assessed using 3 different plasma glucose concentrations. RESULTS: The Passing-Bablok regression for the glucometer against the reference method revealed the presence of both proportional bias (1.12; 95% confidence interval [CI], 1.07-1.18) and constant bias (-11.25; 95% CI, -16.0 to -7.70). The glucometer yielded 92.2%-100% sensitivity and 86.4%-96% specificity for the assessing glucose concentration based on different concentration thresholds. CONCLUSIONS AND CLINICAL IMPORTANCE: The Freestyle Optium Neo H showed proportional and constant biases relative to the reference method. The glucometer showed poor performance according to criteria recommended by the International Standards Organization and the American Society for Veterinary Clinical Pathology. However, the glucometer determined hypoglycemia with high sensitivity and specificity therefore it might be used to diagnose hypoglycemia in calves with different diseases until calf-specific POC glucometers are developed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".