Trends in Short‐, Intermediate‐, and Long‐Term Mortality Following Hospitalization for Myocardial Infarction Among Medicare Beneficiaries, 2008 to 2018
Bibliographic record
Abstract
Background Advances in technology and care quality have transformed the care of acute myocardial infarction (AMI), but little is known about trends in mortality rates across separate time periods after hospitalization. Methods and Results We identified all Medicare fee-for-service beneficiaries hospitalized with incident AMI from 2008 to 2018. We calculated unadjusted mortality rates by dividing the number of all-cause deaths by the number of patients with incident AMI for the following time periods: acute (in hospital), post acute (0-30 days after hospital discharge), short term (31 days to 1 year after discharge), intermediate term (1-2 years after discharge), and long term (2-3 years after discharge). Each period was considered separately (ie, patients who died during one period were not counted in subsequent periods). Using logistic regression to account for differences in patient characteristics, we calculated annual risk standardized mortality ratios defined as observed over expected mortality based on 2008 rates. Among 768 084 patients with incident AMI (mean age 81 years, 48% male, 87% White), declines in observed-to-expected mortality ratios were observed for each time period: acute (0.68 [95% CI, 0.66-0.71]), postacute (0.72 [95% CI, 0.71-0.75]), short term (0.77 [95% CI, 0.75-0.78]), intermediate term (0.79 [95% CI, 0.77-0.81]), and long term (0.77 [95% CI, 0.75-0.79]). Declines were observed both for patients with and without ST-segment-elevation AMI. Conclusions For patients with incident AMI, there have been improvements in mortality rates across periods spanning the hospital stay through 3 years after discharge, reflecting advances in AMI care from hospitalization through long-term outpatient follow-up.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".