Prosthesis-Patient Mismatch Negatively Affects Outcomes after Mitral Valve Replacement: Meta- Analysis of 10,239 Patients
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
OBJECTIVE: This study sought to evaluate the impact of prosthesis-patient mismatch on the risk of perioperative and long-term mortality after mitral valve replacement. METHODS: Databases were researched for studies published until December 2018. Main outcomes of interest were perioperative and 10-year mortality and echocardiographic parameters. RESULTS: The research yielded 2,985 studies for inclusion. Of these, 16 articles were analyzed, and their data extracted. The total number of patients included was 10,239, who underwent mitral valve replacement. The incidence of prosthesis-patient mismatch after mitral valve replacement was 53.7% (5,499 with prosthesis-patient mismatch and 4,740 without prosthesis-patient mismatch). Perioperative (OR 1.519; 95%CI 1.194-1.931, P<0.001) and 10-year (OR 1.515; 95%CI 1.280-1.795, P<0.001) mortality was increased in patients with prosthesis-patient mismatch. Patients with prosthesis-patient mismatch after mitral valve replacement had higher systolic pulmonary artery pressure and transprosthethic gradient and lower indexed effective orifice area and left ventricle ejection fraction. CONCLUSION: Prosthesis-patient mismatch increases perioperative and long-term mortality. Prosthesis-patient mismatch is also associated with pulmonary hypertension and depressed left ventricle systolic function. The findings of this study support the implementation of surgical strategies to prevent prosthesis-patient mismatch in order to decrease mortality rates.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.265 |
| Bibliometrics | 0.002 | 0.002 |
| 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 it