Aortic valve replacement and long-term prognosis.
Bibliographic record
Abstract
OBJECTIVE: We assessed the long-term risk of mortality associated with mechanical and biological aortic valve replacement (AVR). METHODS: We reviewed articles published during the last decade, with emphasis on large series reporting mortality data and follow up of 5 years or longer. The latest editions of textbooks on cardiology and cardiac surgery were also reviewed. We used mortality analysis methodology, comparing the observed mortality in these series to the expected mortality calculated from country specific life tables, to calculate the risk of mortality expressed as the mortality ratio (MR), values above 100% traducing the excess risk of mortality compared to the general population. RESULTS: After AVR, the MR varied from 120% to 350% for mechanical valve prostheses, and from 100% to 300 for bioprosthetic valves according to age at surgery. MR above 400% was associated with an AVR before the age of 50 years. No significant difference in the MR over age 50 years was found between mechanical and biological AVR. Independent prognostic factors after AVR are age at surgery, New York Heart Association (NYHA) functional class at time of surgery, left ventricular ejection fraction (LVEF), atrial fibrillation, and type and severity of valvular lesion. CONCLUSION: No difference was found for excess mortality between mechanical and biological AVR. Under the age of 50 years, higher mortality was associated with both mechanical and bioprosthetic AVR.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".