Heparin dosing is associated with diffusion weighted imaging lesion load following aneurysm coiling
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Diffusion weighted imaging (DWI) may be used to evaluate post-coiling ischemia. Heparinization protocols for cerebral aneurysm coiling procedures differ among operators and centers, with little literature surrounding its effect on DWI lesions. The goal of this study was to determine which factors, including heparinization protocols, may affect DWI lesion load post-coiling. MATERIALS AND METHODS: A review of 135 coiling procedures over 5 years at our centre was performed. Procedural data including length of procedure, number of coils used, stent or balloon assistance and operators were collected. Procedures were either assigned as using a bolus dose (>2000 U at any one time) or small aliquots of heparin (≤2000 U). Postprocedure DWI was reviewed and lesions were classified as small (< 5mm), medium (5-10 mm) or large (>10 mm). The cases were then classified into group 1 (≤5 small lesions) or group 2 (>5 small lesions or ≥1 medium or large lesion). Multivariate regression of the procedural variables for the two groups was calculated. A p value of <0.05 was considered significant. RESULTS: There were 78 procedures in group 1 and 57 procedures in group 2. Patients who received small aliquots (n=37) versus boluses of heparin (n=98) intraprocedurally had significantly greater frequency and size of DWI lesions (p=0.03). None of the other procedural variables was found to impact on lesion load. CONCLUSIONS: More substantial DWI lesions were associated with small aliquots of heparin dosage compared with bolus doses. Heparin boluses should be preferentially administered during aneurysm coiling.
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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 | 0.001 | 0.010 |
| 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 | 0.000 | 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".