Utility of Anti-Xa Monitoring in Children Receiving Enoxaparin for Therapeutic Anticoagulation
Bibliographic record
Abstract
Although enoxaparin is used to treat thromboembolism in children, current treatment guidelines are largely extrapolated from adults. The objectives of this study were to determine: i) correlation between enoxaparin dose and anti-factor Xa (anti-Xa) level, ii) intra-patient variability, and iii) whether dose or anti-Xa level is a predictor of outcomes. A retrospective chart review was conducted on all hospitalized patients receiving enoxaparin in a tertiary care pediatric institution. Simple linear regression, coefficient of variation (CV), and Student's t-test were used to analyze the objectives. Eighty treatment courses with interpretable anti-Xa levels were analyzed. Mean patient age was 6.5 years. Mean enoxaparin dose was 1.10 mg/kg q12h. Correlation between initial dosing and anti-Xa level was poor; R(2) = 0.0307 and 0.0237 for patients > 2 months with and without cardiac or renal diseases, respectively. Four out of seven patients ≤ 2 months of age compared to 4/32 patients > 2 months had a CV > 40%. Similarly, 4/12 cardiac patients compared to 4/27 non-cardiac patients had a CV > 40%. Neither dose nor anti-Xa level predicted treatment success or adverse reactions (P > .05). These results suggest a need to reexamine the use of anti-Xa levels for guiding enoxaparin therapy. Further prospective studies are warranted to clarify whether routine or selective anti-Xa monitoring should be recommended in pediatric patients.
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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 | 0.009 |
| 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 | 0.001 |
| 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".