Pre- and Post-Mortem Characteristics of Lethal Mitral Valve Prolapse Among All Countywide Sudden Deaths
Bibliographic record
Abstract
OBJECTIVES: We sought to investigate the characteristics of mitral valve prolapse (MVP) in a postmortem study of consecutive sudden cardiac deaths (SCDs) up to age 90. BACKGROUND: Up to 2.3% of MVPs suffer SCD, but by convention SCD is rarely confirmed by autopsies. In a postmortem study of young persons < 40 years, 7% of SCDs were caused by MVP; bileaflet involvement, mitral annular disjunction (MAD), and replacement fibrosis were common. METHODS: In the San Francisco POST SCD Study, autopsies have been performed on >1000 consecutive WHO-defined (presumed) SCDs ages 18–90 since 2011, 603 adjudicated. Autopsy-defined sudden arrhythmic death (SAD) required absence of non-arrhythmic cause; MVP diagnosis required leaflet billowing. We reviewed 100 pre-mortem echocardiograms to identify additional MVPs missed on autopsy. RESULTS: Among 603 presumed SCDs, 339 (56%) were autopsy-defined SADs, with MVP identified in 7 (1%). We identified 6 additional MVPs by review of echocardiograms, for a prevalence of at least 2% among 603 presumed SCDs and 4% among 339 SADs (p = 0.02 vs 264 non-SADs). All 6 additional MVPs had monoleaflet rather than bileaflet involvement, and mild mitral regurgitation, ruling out hemodynamic cause. Less than half had MAD with replacement fibrosis, but all had multisite interstitial fibrosis. CONCLUSIONS: In a countywide postmortem study of all adult SCDs, MVP prevalence was at least 4% of SADs, but half were missed on autopsy. Monoleaflet MVP was often underdiagnosed postmortem. Compared to young SCDs, lethal MVP in older SCDs did not consistently have bileaflet anatomy, replacement fibrosis, or MAD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 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".