Contrast-Induced Encephalopathy and the Blood-Brain Barrier
Bibliographic record
Abstract
BACKGROUND: Contrast-induced encephalopathy (CIE) is an adverse event associated with diagnostic and therapeutic endovascular procedures. Decades of animal and human research support a mechanistic role for pathological blood-brain barrier dysfunction (BBBd). Here, we describe an institutional case series and review the literature supporting a mechanistic role for BBBd in CIE. METHODS: A literature review was conducted by searching MEDLINE, Web of Science, Embase, CINAHL and Cochrane databases from inception to January 31, 2022. We searched our institutional neurovascular database for cases of CIE following endovascular treatment of cerebrovascular disease during a 6-month period. Informed consent was obtained in all cases. RESULTS: Review of the literature revealed risk factors for BBBd and CIE, including microvascular disease, pathological neuroinflammation, severe procedural hypertension, iodinated contrast load and altered cerebral blood flow dynamics. In our institutional series, 6 of 52 (11.5%) of patients undergoing therapeutic neuroendovascular procedures developed CIE during the study period. Four patients were treated for ischemic stroke and two patients for recurrent cerebral aneurysms. Mechanical stenting or thrombectomy were utilized in all cases. CONCLUSION: In this institutional case series and literature review of animal and human data, we identified numerous shared risk factors for CIE and BBBd, including microvascular disease, increased procedure length, large contrast volumes, severe intraoperative hypertension and use of mechanical devices that may induce iatrogenic endothelial injury.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".