Peak plasma concentration of direct oral anticoagulants in obese patients weighing over 120 kilograms: A retrospective study
Bibliographic record
Abstract
BACKGROUND: or a weight >120 kg, the use of DOACs in this group is not recommended. OBJECTIVES: To determine the proportion of obese patients with body weight >120 kg with a peak plasma concentration of DOACs lower than the expected median trough level derived from population pharmacokinetic studies for each DOAC. METHODS: Patients with body weight >120 kg taking DOACs for any indication underwent a peak drug concentration measurement at steady state. RESULTS: 38 patients were included in the analysis. The mean age was 64 ± 11 years, and 30 (79%) were males. The median body weight was 132.5 kg (interquartile range [IQR] 127-146.5). The median peak concentrations (IQR) were 148 ng/mL (138-240), 138 ng/mL (123-156.5), 215 ng/mL (181-249) for apixaban, dabigatran, and rivaroxaban, respectively. Two patients (5%, 95% confidence interval [CI]: 0.5%-18%) had a peak plasma concentration lower than the median trough and eight (21%, 95% CI: 11%-37%) had a peak plasma concentration below the fifth percentile (10th percentile for dabigatran) peak concentration. CONCLUSIONS: Most patients in our study had peak plasma concentration higher than the median trough level for each of the three DOACs. However, 21% had a peak plasma concentration that was below the usual on-therapy range of peak concentration for the corresponding DOAC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".