A Comparison of Point-of-Care Instruments Designed for Monitoring Oral Anticoagulation with Standard Laboratory Methods
Bibliographic record
Abstract
Our study compared point-of-care (POC) device monitoring with traditional clinical laboratory methods device of patients on oral anticoagulant therapy. The POC devices used in the study were Coumatrak, CoaguChek, CoaguChek Plus, Thrombolytic Assessment System (TAS) PT-One, TAS PTNC, TAS PT, Hemachron Jr. Signature, ProTime Microcoagulation System, and Medtronics ACT II. The clinical laboratory method used thromboplastins with different ISI values: Innovin and Thromboplastin C Plus (TPC). All POC INRs showed strong correlation with both laboratory methods, with correlation coefficients of >0.900. All POC methods demonstrated a significant (p <0.05) difference in INR values, except the TAS PTNC and ACT II INRs (p: 0.12 and 0.71 respectively) when compared with Innovin INRs. All POC INRs were significantly different from TPC generated INRs (p <0.05). Comparisons of the POC INRs to the group mean of the POC methods, show higher correlation (R>0.93), but there were still significant (p<0.05) differences noted between the POC group INR mean and CoaguChek Plus, ACT II, TAS PT-One, TAS PTNC, and Hemachron Jr Signature INRs. These data indicate that POC INR biases exist between laboratory methods and POC devices. Until a suitable whole blood INR standardization method is available, we conclude that clinicians using point-of-care anticoagulation monitoring should be aware of differences between POC and parent laboratory values.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".