Anticoagulation and bone marrow biopsy: is it safe to proceed?
Bibliographic record
Abstract
BACKGROUND: Bone marrow (BM) biopsy is the most common diagnostic procedure in hematology. Bleeding is an expected complication, and its risk is assumed to be increased in patients on anticoagulants. However, the effect of anticoagulation on BM biopsy safety is unclear and guidelines are lacking robust data in this regard. As such, physicians use their clinical judgement to guide periprocedural management of anticoagulation. OBJECTIVE: To provide the best available evidence regarding management of anticoagulation in patients who need BM biopsy. METHODS: We reviewed and summarized available guidelines directing management of periprocedural anticoagulation for BM biopsy, and share our experience and practices with BM biopsy at our institution. RESULTS: The incidence of significant hemorrhage after BM biopsy is very low (0.007-1.1%). BM biopsy is classified as having a low to moderate bleeding risk. Interrupting anticoagulation is not consistently recommended. Strategies exist to minimize bleeding risk for anticoagulated patients. Patients with myeloproliferative neoplasms can develop an acquired von Willebrand syndrome which increases their risk for bleeding and therefore require extra vigilance to ensure appropriate hemostasis. CONCLUSION: Withholding anticoagulation prior to BM biopsy is not routinely recommended. Instead, assessment and optimization of bleeding risk factors should be done on a patient by patient basis.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".