Management of an unintentional enoxaparin overdose: A case report and literature review
Bibliographic record
Abstract
PURPOSE: The aim of this article is to describe a case in which protamine was used for a low-molecular-weight heparin (LMWH) overdose and present an up-to-date review of the literature on the management of LMWH overdose in adults. SUMMARY: An unintentional administration of enoxaparin 900 mg occurred in a 73-year-old man with coronavirus disease 2019-related pulmonary embolism. Management of the overdose included a protamine bolus followed by an infusion. Anti-factor Xa levels and activated partial thromboplastin time were monitored. Anti-factor Xa levels declined in a linear fashion irrespective of protamine administration. No bleeding or further thrombotic complications occurred in the patient. A review of the literature revealed that the optimal strategy to treat an LMWH overdose is unknown, with treatment of overdoses ranging from clinical observation to aggressive protamine dosing in reported cases. Although protamine effectively neutralizes unfractionated heparin, it is unable to completely reverse LMWH activity and has variable effects on laboratory measures of LMWH anticoagulant activity. CONCLUSION: The current case report provides additional data to previous literature suggesting that protamine may have a limited effect in decreasing anti-factor Xa levels in LMWH overdose. Continued reporting on the management of LMWH overdoses is warranted to clarify the optimal treatment strategy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".