Duplex Ultrasound Screening for Deep Venous Thrombosis in Patients Undergoing Craniotomy for Intracranial Tumors: A Single Institutional Series
Bibliographic record
Abstract
OBJECTIVE: The frequency of duplex ultrasound screening (DUS) for deep vein thrombosis (DVT) in patients with brain tumors undergoing craniotomy is center-specific. We evaluated clinical conditions that increase the tendency to perform DUS, focusing on tumor type. METHODS: This is a single-center retrospective analysis to assess the association of intracranial tumor type with DVT as a major decision-making indicator for DUS. A primary analysis investigated the association between tumor pathology and preoperative DVT, and a secondary analysis investigated the development of DVT postoperatively. Confounding factors were defined and included in both analyses. RESULTS: Among 1478 patients, 751 had preoperative DUS and 35 (5%) had DVT. No significant difference in the odds of preoperative DVT was observed between patients having malignant glioma versus benign tumors (odds ratio [OR; 95% CI]: 1.68 [0.65, 4.35], P = 0.29), or metastatic tumors versus benign tumors (OR: 2.10; 95% CI: 0.75-5.89; P = 0.16). Among patients with negative preoperative DUS, 93 underwent postoperative evaluation and 20 (22%) were diagnosed with postoperative DVT. Malignant glioma or (OR: 1.69; 95% CI: 0.36-7.84; P = 0.50) metastatic tumors (OR: 1.84; 95% CI: 0.29-11.5; P = 0.52) were not associated with postoperative DVT versus benign tumors. CONCLUSION: Brain tumor pathology may not increase the risk for DVT and may not be a good indicator for the selection of patients for DVT screening with DUS. The incidence of DVT in selective preoperative DUS was similar to studies that performed DUS on all patients. Further studies across multiple institutions are needed to develop criteria for DUS in brain tumor surgery.
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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 | 0.003 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".