Comparison between the conventional method and a portable device for determination of INR
Bibliographic record
Abstract
CONTEXT: Anticoagulation with warfarin is considered the appropriate treatment for venous thromboembolism and other thrombotic pathologies. Regular INR control is required for dosage adjustment and therapeutic control. Use of portable monitoring systems optimizes management of these patients. OBJECTIVE: To compare INR measurements taken using the portable Coaguchek XS system in capillary blood with the standard laboratory method using venous blood. METHOD: Fifty-two samples each of venous and capillary blood were collected from nineteen patients on warfarin, who had been admitted to the Hospital da Beneficência Portuguesa de São Paulo, and analyzed using the conventional method and the Coaguchek XS system, respectively. RESULTS: Spearman's correlation coefficient ® for the overall performance of the two methods was 0.978 (p<0.0001; 95%CI 0.961-0.988). The Kappa measure of agreement for all patients was 76.8% (p<0.001; IC: 95% 0.975-0.561). Mean INR according to the Coaguchek XS system underestimated the values provided by the conventional method by -0.01 INR points, with a standard error of 0.342. Results for INR values greater than 3.5 were satisfactory with a correlation coefficient of 0.71, but without statistical significance (p>0.714). CONCLUSIONS: The Coaguchek XS system can be used to monitor prothrombin time in patients on oral anticoagulants, provided INR values greater than 3.5 are confirmed using the conventional laboratory method.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".