Structural valve deterioration after transcatheter aortic valve implantation
Bibliographic record
Abstract
BACKGROUND: Transcatheter aortic valve implantation (TAVI), widely used to treat high-risk patients with severe symptomatic aortic stenosis, has recently been extended to younger patients at lower operative risk in whom long-term durability of TAVI devices is an important concern. Therefore, we conducted a systematic review and meta-analysis of observational studies addressing the frequency of structural valve deterioration (SVD) after TAVI. METHODS: We searched Medline, Embase, Cochrane Database of Systematic Reviews, and Cochrane CENTRAL from 2002 to September 2016. We included observational studies following patients with TAVI for at least 2 years. Independently and in duplicate, we evaluated study eligibility, extracted data, and assessed risk of bias for SVD post-TAVI. Our review used the GRADE system to assess quality of evidence. We pooled incidence rates using a random effects model. RESULTS: Thirteen studies including 8914 patients, with a median follow-up between 1.6 and 5 years, reported an incidence of SVD post-TAVI between 0 to 1.34 per 100 patient years. The pooled incidence of SVD was 28.08 per 10 000 patients/year (95% CI 2.46 to 73.44 per 100 patient years). Of those who developed SVD, 12% underwent valve re-intervention. Confidence in the evidence was moderate due to inconsistency among studies. CONCLUSION: Structural valve deterioration is probably an infrequent event within the first 5 years after TAVI. Ascertaining the impact of SVD and the need for valve-related re-interventions to inform recommendations for patients with a longer life-expectancy will require studies including a large number of patients with longer follow-up (>10 years).
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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 | 0.018 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.013 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".