Accuracy and Precision of Point‐of‐Care International Normalized Ratio in Patients With Liver Disease
Bibliographic record
Abstract
OBJECTIVE: To determine if the CoaguChek XS Pro Point-of-Care (POC) device can accurately and precisely measure the international normalized ratio (INR) compared with the gold standard laboratory INR in pediatric and adult patients with liver disease. METHODS: This prospective cohort study included 15 pediatric patients without liver disease, 13 pediatric patients with liver disease, and 17 adult patients with liver disease. The accuracy of the POC INR values was determined using the correlation and Bland-Altman limits of agreement. The accuracy of the coagulometer INR was assessed by calculating the proportion of POC INR measurements that were ≤15% of their corresponding laboratory INR. RESULTS: A comparison of INR measurements showed an excellent correlation in pediatric patients without liver disease ( r = 0.82), pediatric patients with liver disease ( r = 0.89), and adult patients with liver disease ( r = 0.96). Fourteen (93%) POC INR values were ≤15% in pediatric patients without liver disease from its paired laboratory INR. All 13 paired measurements were ≤15% in pediatric patients with liver disease. In adult patients with liver disease, 12 (71%) POC INR values were ≤15% of their paired laboratory INR. CONCLUSIONS: In patients with liver disease, the CoaguChek XS Pro provides an accurate measure of the INR compared to laboratory INR measurements.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".