TAVI or No TAVI: identifying patients unlikely to benefit from transcatheter aortic valve implantation
Bibliographic record
Abstract
Transcatheter aortic valve implantation (TAVI) has spawned the evolution of novel catheter-based therapies for a variety of cardiovascular conditions. Newer device iterations are delivering lower peri- and early post-procedural complication rates in patients with aortic stenosis, who were otherwise deemed too high risk for conventional surgical valve replacement. Yet beyond the post-procedural period, a considerable portion of current TAVI recipients fail to derive a benefit from TAVI, either dying or displaying a lack of clinical and functional improvement. Considerable interest now lies in better identifying factors likely to predict futility post-TAVI. Implicit in this are the critical roles of frailty, disability, and a multimorbidity patient assessment. In this review, we outline the roles that a variety of medical comorbidities play in determining futile post-TAVI outcomes, including the critical role of frailty underlying the identification of patients unlikely to benefit from TAVI. We discuss various TAVI risk scores, and further propose that by combining such scores along with frailty parameters and the presence of specific organ failure, a more accurate and holistic assessment of potential TAVI-related futility could be achieved.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".