The application of Silenz MR angiography in the follow-up assessment of intracranial aneurysms embolization
Bibliographic record
Abstract
Objectives To explore the value of Silenz MRA in the follow-up assessment of intracranial aneurysms embolization. Methods Fifteen patients underwent coiled embolization were prospectively collected. Silenz and time of flight MRA (TOF MRA) were performed on the same day as DSA examination. Two neuro-radiologists scored the structures of peripheral vascular with a 4-score grading system and evaluate embolism status (two-grade montreal scale). The scores of vascular structures were compared using Wilcoxon signed rank tests. Weighted Kappa statistics was used to assess the inter-observer agreement on each MRA scoring, the inter-modality agreement between MRA and DSA, the inter-modality agreement between the MRA methods. Results There were 11 cases with complete occlusion, 4 cases with residual aneurysm revealed by DSA. For depiction, Silenz MRA was significantly better than TOF MRA [(3.50±0.62) vs. (3.00±0.63), Z=-3.12, P=0.002]. Inter-modality agreement of Silenz MRA and DSA was excellent (Kappa=0.82), while the agreement of TOF MRA and DSA was moderate(Kappa=0.60). Inter-modality agreement between Silenz MRA and TOF MRA was good (Kappa=0.76). Conclusions Silenz MRA is superior to TOF MRA for depiction of vascular structures and evaluation of embolism status, which is highly related with DSA. It has the value in the postoperative follow-up evaluation. Key words: Magnetic resonance angiography; Intracranial aneurysm; Embolization, therapeutic; Comparative study
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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 | 0.001 | 0.001 |
| 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".