Impact of cerebral protection on observed versus predicted in-hospital stroke in a high stroke risk TAVR cohort
Bibliographic record
Abstract
BACKGROUND: Despite impressive improvements in the safety profile of Transcatheter aortic valve replacement (TAVR), the risk for peri-procedural stroke after TAVR has not declined substantially. In an effort to reduce periprocedural stroke, cerebral embolic protection (CEP) devices have been utilized but have yet to demonstrate benefit in all-comers. There is a paucity of data supporting the utilization of CEP in TAVR patients with an anticipated high risk for peri-procedural stroke. METHODS: The Transcatheter Aortic Valve Replacement In-Hospital Stroke (TASK) score is a clinical risk tool for predicting the in-hospital stroke risk of patients undergoing transfemoral TAVR. This score was used to identify high-risk patients and calculate the expected in-hospital stroke risk. This was a single-centre cohort study in all consecutive TAVR patients who had placement of CEP. The observed versus expected ratio for peri-procedural stroke was calculated. To obtain 95% credible intervals, we used 1000 bootstrapped samples of the original cohort sample size without replacement and recalculated the TASK predicted scores. RESULTS: The study included 103 patients. The median age was 83 (IQR 78,89). 63 were male (61.1%) and 45 (43.69%) had a history of previous Stroke or TIA. Two patients had an in-hospital stroke after TAVR (1.94%). The expected risk of in-hospital stroke based on the TASK score was 3.39% (95% CI 3.07-3.73). The observed versus expected ratio was 0.57 (95% CI 0.52-0.64). CONCLUSION: In this single-center study, we found that in patients undergoing TAVR with high stroke risk, CEP reduced the in-hospital stroke risk by 43% when compared with the risk-score predicted rate. CLINICAL TRIAL NUMBER: N/A.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".