Edge-to-edge repaire and the incidence of silent cerebral embolic events
Bibliographic record
Abstract
Abstract Background Silent cerebral hits are present in the majority of patients (75-80%) undergoing TAVI. However, there is a lack of data on the rate of silent cerebral infarcts after percutaneous "edge-to-edge" procedure. Purpose To assess clinically overt stroke and silent cerebral embolic lesions detected by diffusion-weighted magnetic resonance imaging (DW-MRI) in patients after MitraClip implantation. Methods and results 85 patients underwent MitraClip implantation at our institution within a period of May 2016 – January 2023. Of these, 53 patients (62%) underwent DW-MRI pre (<24 hours) and post (24-48 hours) "edge-to-edge" procedure. No patients showed a significant decline in post-neurocognitive function (as assessed by the Montreal Cognitive Assessment [MoCA] score) compared with baseline. DW-MRI detected silent cerebral lesion in 11 patients (21%; 6 unilateral and 5 bilateral) after MitraClip implantation. Conclusions The rate of silent (assessed by DW-MRI) cerebral embolic events after MitraClip implantation was very low in our observational study (the largest study worldwide so far). Larger randomized trials including neuroimaging are needed to define the occurrence and consequences of cerebral damage during transcatheter treatment of mitral regurgitation. Future research is also needed to investigate the role of cerebrovasczlar protection devices during this procedure.
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".