Thromboembolic prophylaxis in neurosurgical practice: a systematic review
Bibliographic record
Abstract
BACKGROUND: In neurosurgical patients, the risk of developing venous thromboembolism (VTE) is high due to the relatively long duration of surgical interventions, usually long immobilization time after surgery, and possible neurological deficits which can negatively influence mobility. In neurosurgical clinical practice, there is lack of consensus on optimal prophylaxis against VTE, mechanical or pharmacological. OBJECTIVE: To systematically review available literature on the incidence of VTE in neurosurgical interventions and to establish an optimum prevention strategy. METHODS: A literature search was performed in PubMed, Embase, Web of Science, Cochrane Library, and EmCare, based on a sensitive search string combination. Studies were selected by predefined selection criteria, and risk of bias was assessed by Newcastle-Ottawa Quality Assessment Scale and Cochrane risk of bias. RESULTS: Twenty-five studies were included, half of which had low risk of bias (21 case series, 3 comparative studies, 1 RCT). VTE was substantially higher if the evaluation was done by duplex ultrasound (DUS), or another systematic screening method, in comparison to clinical evaluation (clin). Without prophylaxis DVT, incidence varied from 4 (clin) to 10% (DUS), studies providing low molecular weight heparin (LMWH) reported an incidence of 2 (clin) to 31% (DUS), providing LMWH and compression stockings (CS) reported an incidence of 6.4% (clin) to 29.8% (DUS), and providing LMWH and intermittent pneumatic compression devices (IPC) reported an incidence of 3 (clin) to 22.3% (DUS). Due to a lack of data, VTE incidence could not meaningfully be compared between patients with intracranial and spine surgery. The reported incidence of pulmonary embolism (PE) was 0 to 7.9%. CONCLUSION: Low molecular weight heparin, compression stockings, and intermittent pneumatic compression devices were all evaluated to give reduction in VTE, but data were too widely varying to establish an optimum prevention strategy. Systematic screening for DVT reveals much higher incidence percentages in comparison to screening solely on clinical grounds and is recommended in follow-up of neurosurgical procedures with an increased risk for DVT development in order to prevent occurrence of PE.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".