Impact of surgical aortic root enlargement on the outcomes of aortic valve replacement: a meta-analysis of 13 174 patients
Bibliographic record
Abstract
OBJECTIVES: This study sought to evaluate the impact of surgical aortic root enlargement (ARE) on the perioperative outcomes of aortic valve replacement (AVR). METHODS: Databases were searched for studies published until April 2018 to carry out a systematic review followed by meta-analysis of results. RESULTS: The search yielded 1468 studies for inclusion. Of these, 10 articles were analysed and their data extracted. A total of 13 174 patients (AVR with ARE: 2819 patients; AVR without ARE: 10 355 patients) were included from studies published from 2002 to 2018. The total rate of ARE was 21.4%, varying in the studies from 5.7% to 26.3%. The overall odds ratio (OR) [95% confidence interval (CI)] for perioperative mortality showed a statistically significant difference between the groups (among 10 studies), with a higher risk in the 'AVR with ARE' group (OR 1.506, 95% CI 1.209-1.875; P < 0.001), but not when adjusted for isolated AVR + ARE without any concomitant procedures such as mitral valve surgery, coronary artery bypass surgery, etc. (OR 1.625, 95% CI 0.968-2.726; P = 0.066-among 6 studies). The 'AVR with ARE' group showed an overall lower risk of significant patient-prosthesis mismatch among 9 studies (OR 0.472, 95% CI 0.295-0.756; P = 0.002) and a higher overall difference in means of indexed effective orifice area among 10 studies (random-effect model: 0.06 cm2/m2, 95% CI 0.029-0.103; P < 0.001). CONCLUSIONS: Surgical ARE seems to be associated with increased perioperative mortality but with lower risk of patient-prosthesis mismatch.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.041 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".