PREDICT: Precise deployment of Silk Vista Baby in confined territory: A technical note
Bibliographic record
Abstract
Flow diverters (FD) have become increasingly useful in treating complex intracranial aneurysms, particularly wide-necked and recurrent aneurysms. Their use has progressively expanded to smaller vessels beyond the circle of Willis (CoW), and Silk Vista Baby (SVB) is one such low-profile FD which stands out because of deliverability through a 0.017″ microcatheter and smoother navigability. Precise deployment of SVB, specifically, the proximal end, can be challenging in certain anatomical locations when the proximal landing zone is very short, limited by vessel bifurcation or important branches arising from the artery or its geometry. We present our series to describe our technique and rule to 'PREDICT' the final deployment of SVB in real time, and discuss the nuances, exceptions and bail-out strategies. Using this technique, we were able to precisely deploy SVB in distal intracranial vessels with a mean proximal landing zone as short as 2.6 mm in 80% instances, requiring bail-out strategies in only 20% cases. This rule can be reliably followed in treating complex intracranial aneurysms with SVB FD within a confined territory, until validated software-based real-time planning tools are developed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".