Is Transcatheter Aortic Valve Implantation Effective in Improving Quality of Life?
Bibliographic record
Abstract
BACKGROUND: Aortic stenosis (AS) without surgical intervention is associated with morbidity and mortality and is the most common valvular disease in the western world. Transcatheter aortic valve implantation (TAVI) is a minimally invasive surgical option that has become a common treatment for people unable to undergo open aortic valve replacement; despite the increase in TAVI offerings in the last decade, patient quality of life (QoL) outcomes postoperatively are poorly understood. OBJECTIVE: The aim of this review was to determine whether TAVI is effective in improving QoL. METHOD: A systematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted, and the protocol was registered on PROSPERO (CRD42019122753). MEDLINE, CINAHL, EMBASE, and PsycINFO were searched for studies published between 2008 and 2021. Search terms included "transcatheter aortic valve replacement" and "quality of life" and their synonyms. Included studies were evaluated, dependent on study design, using either the Risk of Bias-2 or the Newcastle-Ottawa Scale. Seventy studies were included in the review. RESULTS: Authors of the studies used a wide variety of QoL assessment instruments and follow-up durations; authors of most studies identified an improvement in QoL, and a small number identified a decline in QoL or no change from baseline. CONCLUSION: Although authors of the vast majority of studies identified an improvement in QoL, there was very little consistency in instrument choice or follow-up duration; this made analysis and comparison difficult. A consistent approach to measuring QoL for patients who undergo TAVI is needed to enable comparison of outcomes. A richer, more nuanced understanding of QoL outcomes after TAVI could help clinicians support patient decision making and evaluate outcomes.
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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 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".