Early Aortic Valve Replacement versus Watchful Waiting in Asymptomatic Severe Aortic Stenosis: A Study-Level Meta-Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BackgroundThe management of patients with asymptomatic, severe aortic stenosis (AS) is controversial. We performed a meta-analysis to examine the impact on outcomes of early aortic valve replacement (AVR) in patients with severe asymptomatic AS versus a watchful-waiting (WW) approach.MethodsDatabases were searched for studies published until April 2019. Main outcome of interest was death during follow-up.ResultsThe search yielded 1,889 studies for inclusion. Of these, seven articles were analyzed and their data extracted. The total number of patients included was 3,839. The overall HR (95% CI) for death showed a statistically significant difference between the groups, with lower risk in the “early AVR” group (random effect model: HR 0.280; 95% CI 0.159–0.494, P < 0.001). There was evidence of significant statistical heterogeneity of treatment effect among the studies for death. Funnel plot analysis disclosed no asymmetry around the axis for the outcome of interest, which means that we have low risk of publication bias related to this outcome. Sensitivity analysis showed that none of the studies had a particular impact on the results. The meta-regression coefficients for the modulating factors age, male sex, presence of hypertension and presence of diabetes were significant for mortality, showing that the early intervention becomes even more protective in comparison with the conservative approach when we take these factors into consideration.ConclusionEarly AVR seems to be a better approach than WW in the treatment of asymptomatic patients with severe AS, but we would still advocate a case-by-case decision-making process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it