Quantifying neurological function in patients undergoing transcatheter aortic valve implantation
Bibliographic record
Abstract
Background: Transcatheter aortic valve implantation (TAVI) is a routine procedure that is often performed on older adults that are high-risk patients with severe aortic stenosis. Patients after TAVI may experience neurological complications. However, there is a lack of objective neurological testing available for patients undergoing cardiac surgery. Objective: This brief communication seeks to explore the use of robotic technology to quantify distinctive patterns of visuospatial, sensorimotor, and cognitive functioning in patients undergoing TAVI. Methods: Patients undergoing TAVI were recruited for this prospective observational study. Prior to their procedure, study participants performed four robotic reaching tasks using the Kinarm robotic system. Patients repeated the assessment three months after their TAVI procedure. Significant changes in overall task score and parameters were determined. Results: Ten patients were recruited and included in this brief report. In a simple reaching task, patients show significant improvement in performance post-TAVI. However, patients do not improve nor worsen in a complex reaching task after TAVI. Similarly, patients demonstrate impairments in both trail making tasks before and after their TAVI procedure. Conclusions: This study captures the variability in neurological functioning in older patients undergoing TAVI. Robotic technology and quantified assessment procedures can be extremely valuable for detecting perioperative neurological impairments in this patient population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".