Transcatheter aortic valve replacement in low risk patients
Bibliographic record
Abstract
Transcatheter aortic valve replacement (TAVR) is a relatively new technology that has grown exponentially over the past decade. Although it was initially restricted to elderly patients at very high or prohibitive surgical risk, it is currently being evaluated as a treatment option in younger and lower risk patients. The increasing experience of the Heart Teams, along with the continued refinement of transcatheter valve technology has resulted in TAVR achieving results comparable to those of surgery for treating intermediate-risk patients. Furthermore, promising preliminary results have been obtained from observational and propensity matched studies in low risk patients, and a small randomized trial showed the non-inferiority of TAVR vs. SAVR regarding early and late (up to 6 years) outcomes. Three ongoing randomized trials will provide the definite response about the safety and efficacy of TAVR for treating low risk patients with severe aortic stenosis in the near future. The (expected) positive results of these studies would establish the basis for TAVR as the preferred treatment for the majority of patients with aortic stenosis. However, continuous research efforts for better determining valve durability among TAVR recipients, as well as reducing some of the genuine and frequent complications of TAVR (e.g. conduction disturbances) are important in this final effort for making TAVR the default treatment for aortic stenosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".