Safety of Abciximab injection during endovascular treatment of ruptured aneurysms
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: We aimed to determine the safety of intra-arterial Abciximab injection in the management of thromboembolic complications during endovascular treatment of ruptured cerebral aneurysms. METHODS: In a monocentric consecutive series of endovascular treatment of 783 ruptured aneurysms, 42 (5.3%) patients received Abciximab after the aneurysm was secured. Bleeding complications were registered and dichotomized as follows: new intracranial hemorrhage and peripheral bleeding. For each patient, World Federation of Neurosurgery (WFNS) subarachnoid hemorrhage (SAH) grade, shunting, and clinical outcomes in the post-operative period and at 3-6 months were recorded. RESULTS: SAH WFNS grades were as follows: grade I n = 14, grade II n = 10, grade III n = 11, grade IV n = 4, grade V n = 3. Ten patients had intracranial hematoma additionally to the SAH prior to embolization. Four patients (9.5%) presented more blood on the post-embolization CT but only one suffered a new clinically relevant intracranial hemorrhage. Two patients (4.8%) experienced significant peripheral bleeding but none were associated with long-term disabilities. Fourteen patients had a shunt installed less than 24 h prior to Abciximab injection and one less than 48 h later. At 3-6-month follow-up, 31 patients (74%) achieved a modified Rankin Scale score (mRS) of 2 or less, six patients (14%) had a mRS of 3-5, three were dead (7%), and two were lost at follow-up. CONCLUSION: When the aneurysm is secured, intra-arterial Abciximab injection is a low complication rate treatment modality for thromboembolic events during embolization of cerebral ruptured aneurysm.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".