MR Angiography Compared to Conventional Selective Angiography in Acute Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Accuracy of intracranial magnetic resonance angiography (MRA) and reliability of interpretation are not well established compared to conventional selective catheter angiography. The purpose of this study was to determine the accuracy of MRA in evaluation of intracranial vessels in acute stroke and transient ischemic attack (TIA) patients METHODS: Twenty-nine patients (seven females, 22 males; median age 53) with acute stroke or TIA were enrolled into the study. All patients underwent both MRA using a 3 T clinical magnet and conventional angiography within 48 hours. Median time between MRA and angiography was 263 minutes. Conventional angiography preceded MRA in 15 cases. Fourteen patients received thrombolysis during MRA or angiography. National Institutes of Health Stroke Scale scores were obtained prior to the MR exam. One neuroradiologist rated all conventional angiograms, which were used as gold standard. Five observers, blinded to conventional angiography results and all clinical information except symptom side, rated the MR angiograms. Kappa statistics were used to assess reliability; contingency tables were used to assess accuracy of non-enhanced and enhanced MRA. RESULTS: Two hundred and fifty two intracranial vessels were assessed. Agreement between raters was good for both non-enhanced (kappa = 0.50) and gadolinium-enhanced (kappa = 0.46) images. There were a total of 26 vessels occluded by DSA. Overall, the non-enhanced MRA showed sensitivity of 84.2% (95% CI 60.4-96.6) and specificity of 84.6% (95% CI 78.6-89.4). The enhanced MRA showed sensitivity of 69.2 (95% CI 38.6-90.9) and specificity of 73.6 (95% CI 65.5-80.7). CONCLUSIONS: Magnetic resonance angiography is a good non-invasive screening tool for assessing intracranial vessel status in acute ischemic stroke. Angiography remains the gold standard for definitive assessment of the intracranial circulation.
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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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".