Monitoring Direct Thrombin Inhibitors With Calibrated Diluted Thrombin Time vs Activated Partial Thromboplastin Time in Pediatric Patients
Bibliographic record
Abstract
OBJECTIVES: Activated partial thromboplastin time (aPTT) is the primary test used to monitor intravenous (IV) direct thrombin inhibitors (DTIs) but has many limitations. The plasma diluted thrombin time (dTT) has shown better correlation with DTI levels than aPTT. This study compared dose-response curves for dTT and aPTT in pediatric patients receiving argatroban and bivalirudin. METHODS: A retrospective review of pediatric patients treated with argatroban (n = 45) or bivalirudin (n = 14) monitored with dTT and aPTT. RESULTS: The dTT assay was calibrated to report DTI concentrations in µg/mL for argatroban and bivalirudin with good analytic sensitivity and specificity. The dTT was fivefold more likely to show a stable dose-response slope than the aPTT (P < .0002; odds ratio, 4.9). For patients in whom both dTT and aPTT showed a significant correlation between dose and assay results, dTT had a higher average correlation factor compared with aPTT (P = .007). Argatroban dose-response slopes showed more inter- and intrapatient variation than bivalirudin (dose-response slope coefficient of variation, 132% vs 52%). CONCLUSIONS: The dTT assay was more likely to show a stable dose response and have a stronger correlation with DTI dose than aPTT. Argatroban shows more variation in dose response than bivalirudin.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".