Surgical aortic valve replacement and patient–prosthesis mismatch: a meta-analysis of 108 182 patients
Bibliographic record
Abstract
OBJECTIVES: This study sought to evaluate the impact of patient-prosthesis mismatch (PPM) on the risk of perioperative, early-, mid- and long-term mortality rates after surgical aortic valve replacement. METHODS: Databases were searched for studies published until March 2018. The main outcomes of interest were perioperative mortality, 1-year mortality, 5-year mortality and 10-year mortality. RESULTS: The search yielded 3761 studies for inclusion. Of these, 70 articles were analysed, and their data were extracted. The total number of patients included was 108 182 who underwent surgical aortic valve replacement. The incidence of PPM after surgical aortic valve replacement was 53.7% (58 116 with PPM and 50 066 without PPM). Perioperative mortality [odds ratio (OR) 1.491, 95% confidence interval (CI) 1.302-1.707; P < 0.001], 1-year mortality (OR 1.465, 95% CI 1.277-1.681; P < 0.001), 5-year mortality (OR 1.358, 95% CI 1.218-1.515; P < 0.001) and 10-year mortality (OR 1.534, 95% CI 1.290-1.825; P < 0.001) were increased in patients with PPM. Both severe PPM and moderate PPM were associated with increased risk of perioperative mortality, 1-year mortality, 5-year mortality and 10-year mortality when analysed together and separately, although we observed a higher risk in the group with severe PPM. CONCLUSIONS: Moderate/severe PPM increases perioperative, early-, mid- and long-term mortality rates proportionally to its severity. The findings of this study support the implementation of surgical strategies to prevent PPM in order to decrease mortality rates.
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.
How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.043 |
| Bibliometrics | 0.002 | 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) | 0.000 | 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 teacher head, 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".