Neurologic Complications of Cerebral Angiography: Prospective Analysis of 2,899 Procedures and Review of the Literature
Bibliographic record
Abstract
PURPOSE: To prospectively identify risk factors for neurologic complications related to cerebral angiography. MATERIALS AND METHODS: A total of 2,899 consecutive cerebral digital subtraction angiograms obtained with nonionic contrast material were prospectively evaluated. Neurologic complications were categorized as transient (<24 hours), reversible (24 hours to 7 days), and permanent (>7 days). The neurologic complication rate was correlated with patient age, type of indication for catheter angiography, medical history, fluoroscopic time, number and size of catheters, type and number of vessels injected, operator experience, and the quartile in which the study was performed. The correlations were statistically analyzed with Fisher exact tests and a multiple logistic regression model. RESULTS: There were 39 (1.3%) neurologic complications in 2,899 procedures; 20 were transient (0.7%), five (0.2%) were reversible, and 14 (0.5%) were permanent. Neurologic complications were significantly more common in patients 55 years of age or older (25 of 1,361; 1.8%) (P =.035), in patients with cardiovascular disease (CVD) (20 of 862; 2.3%) (P =.004), and when fluoroscopic times were 10 minutes or longer (24 of 1,238; 1.9%) (P =.022). The neurologic complication rate was higher in procedures performed by fellows alone (24 of 1,878; 1.3%) compared with that when staff alone performed the procedures (three of 598; 0.5%), but the difference was not significant (P =.172). Neurologic complications were lower in the fourth quartile of the study (six of 171; 0.9%) compared with the first quartile (16 of 776; 2.1%), which was likely due to fewer patients being examined for carotid stenosis or ischemic stroke and fewer patients with CVD (P =.085). CONCLUSION: Age-related vascular disease accounted for the failure to lower the neurologic complication rate of cerebral angiography despite technical advances.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".