Coagulation Assays in Patients with New Oral Anticoagulants (<scp>NOACs</scp>): Why? When?
Bibliographic record
Abstract
ABSTRACT Clinical Development Phases I‐III Regulatory, Quality, Manufacturing Treatment with new oral anticoagulant drugs does not require laboratory monitoring. However, there are some clinical settings where the measurement of the anticoagulant activity can be useful, especially in urgent surgery or invasive procedure. Prothrombin time ( PT ) and activated partial thromboplastin time ( aPTT ) are prolonged with good correlation with plasma drug concentration. The influence of rivaroxaban on PT is greater than that on aPTT , in contrast to dabigatran. Apixaban responses to PT and aPTT are weak. These tests are not specific and cannot detect low plasma concentrations. For direct Factor‐ X a inhibitors, the most appropriate test is a modified anti‐ X a chromogenic assay. A specific test unresponsive to heparins and fondaparinux has been developed. For dabigatran measurement, a modified thrombin clotting time, “Hemoclot,” has been well documented. The use of plasma calibrators allows an expression of the results in ng/ml of plasma. Expected peak concentration (2 to 4 h after drug intake) and trough concentration or Cmin—before a repeated administration—have been determined. More clinical work is warranted to determine possible adjustments of the drug according to its plasma concentration. Finally, the potential advantage of a punctual screening during long‐term treatment has not been investigated.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".