Venous Thromboembolism Prophylaxis in Patients Undergoing Cranial Neurosurgery: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Randomized clinical trials (RCTs) have usually supported using heparin prophylaxis against venous thromboembolism (VTE) in patients undergoing cranial neurosurgery. The tradeoff between benefit and bleeding risk, however, has not been adequately characterized. OBJECTIVE: To conduct a systematic review and meta-analysis assessing the extent to which low-dose unfractionated heparin (LDUH) or low-molecular-weight heparin (LMWH) prophylaxis reduces the rate of VTE and increases the rate of intracerebral hemorrhage (ICH) and other bleeding in patients undergoing elective cranial neurosurgery. METHODS: We selected RCTs that evaluated LDUH or LMWH prophylaxis of VTE in patients undergoing elective cranial neurosurgery. A meta-analysis assessing heparins vs no heparin (either with or without mechanical methods) was performed. RESULTS: Eight RCTs were identified. Six RCTs involving 1170 patients evaluated LDUH or LMWH vs a control group. Five of 6 trials found a significant reduction in the risk of symptomatic and asymptomatic VTE with heparin prophylaxis. The pooled risk ratio was 0.58 (95% confidence interval, 0.45-0.75). ICH was more common in those receiving heparin, but not statistically significantly. For every 1000 patients who receive heparin prophylaxis, 91 VTE events will be prevented (approximately 35 of which are proximal deep vein thrombosis or pulmonary embolism and 9 to 18 of which are symptomatic), whereas 7 ICHs and 28 more minor bleeds will occur. CONCLUSION: Heparin prophylaxis for patients undergoing elective cranial neurosurgery reduces the risk of VTE but may also increase bleeding risks with a ratio of serious or symptomatic VTE relative to serious bleeding that is only slightly favorable.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.030 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".