Predictive Value of Plasma D-Dimer for Cerebral Herniation Post-Thrombectomy in Acute Ischemic Stroke Patients
Bibliographic record
Abstract
Background: Cerebral hernia is a serious complication after thrombectomy in patients with acute ischemic stroke (AIS). This study aims to explore the predictive value of emergency preoperative plasma D-dimer levels in cerebral herniation after successful thrombectomy. Methods: Between January 2019 and December 2023, patients with AIS who received a successful thrombectomy in our single comprehensive stroke center were retrospectively enrolled. We conducted a statistical analysis on the data using SPSS 26.0. Receiver operating characteristic curve (ROC) was used to evaluate the predictive value of D-dimer level for cerebral herniation. Results: Among 278 enrolled patients, 20 cases (7.19%) experienced cerebral herniation. In patients with cerebral hernia, the score of the National Institutes of Health Stroke Scale was higher (16.5 vs 12.0, P < 0.001), the Alberta Stroke Plan early CT score was lower (6.5 vs 8.0, P < 0.001), the score of collateral circulation was lower (2.0 vs 3.0, P < 0.001), the proportion of eTICI blood flow grading of 3 of the occluded vessel was less (35% vs 75.19%), the proportion of pathogenesis of large atherosclerosis was less (5.00% vs 46.51%, P < 0.001), and the level of plasma D-dimer was higher (2.61 vs 0.82). After adjusting for potential confounders, the level of D-dimer (adjusted OR = 1.131, 95% CI 1.022-1.250, P = 0.017) was significantly correlated with cerebral hernia. Based on the ROC curve, the sensitivity and specificity of D-dimer in predicting cerebral herniation were 75.0% and 73.3%, respectively, and the area under the curve was 0.766. Conclusion: Although our study had certain limitations, we found that elevated emergency preoperative plasma D-dimer level is an independent predictive factor for the cerebral herniation after successful thrombectomy in patients with AIS, which is of great clinical significance.
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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 | 0.004 |
| 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 | 0.001 |
| 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".