Comparison of pre- and postcontrast 3D time-of-flight MR angiography for the evaluation of distal intracranial branch occlusions in acute ischemic stroke.
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Three-dimensional time-of-flight (TOF) MR angiography is used routinely in stroke workup to detect arterial occlusions, but a major drawback is its inadequate depiction of vessels with slow or in-plane flow. We hypothesized that the use of contrast-enhanced MR angiography improves delineation of vessels with diminished or absent flow on precontrast MR angiograms. METHODS: Pre- and postcontrast 3D TOF MR angiograms were acquired in 55 consecutive patients with acute stroke. Patency of 480 intracranial vessels was assessed on both the pre- and postcontrast angiograms. Diffusion-weighted (DW) and perfusion-weighted (PW) imaging data were also obtained and results correlated with those of pre- and postcontrast MR angiography. RESULTS: For 50 abnormal vessel segments seen on precontrast MR angiograms, postcontrast MR angiograms resulted in change in the vascular signal intensity in 70% (35 vessel segments); 94% of these changes showed a greater extent of vessel patency. Venous and soft-tissue contrast enhancement had no effect on assessment in 95% of all 480 vessels examined. Interobserver reliability was moderate, with postcontrast interpretation (kappa = 0.48) showing a slight improvement over precontrast interpretation (kappa = 0.41). Good agreement was found between the TOF results and the pooled DW and PW imaging results. CONCLUSIONS: Compared with precontrast 3D TOF MR angiograms, postcontrast 3D TOF angiograms improve assessment of intracranial vessel patency in acutely ischemic vascular territories. In some patients, an improved understanding of acute ischemic stroke was obtained by viewing the pre- and postcontrast images. Postcontrast MR angiography should be included in the MR evaluation of acute stroke.
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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 | 0.008 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".