Relationship Between Care Gaps and Projected Life Expectancy After Acute Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: Higher-risk patients may not receive evidence-based therapy because of limited life expectancy, which is a composite measure that encompasses many patient factors, including age, frailty, and comorbidities. In this study, we evaluated the extent to which treatment care gaps can be explained by a difference in projected life expectancy. METHODS AND RESULTS: An observational cohort study was conducted on acute myocardial infarction patients hospitalized in Ontario, Canada. Projected life expectancy was estimated using actual survival data with extrapolation using proportional hazard models adjusting for important covariates. The relationship between projected life expectancy with statins and reperfusion therapy was examined using generalized linear models. Among the 7001 acute myocardial infarction patients, 84.3% were prescribed statins and 72.9% were treated with reperfusion therapy. When projected life expectancy was <10 years, the likelihood of receiving either treatment declined progressively with reduction in life expectancy (P<0.001). At the 25th percentile of projected life expectancies, the likelihood of receiving a statin decreased by 1.4% (95% confidence interval, 1.0-1.8%), and acute reperfusion therapy decreased by 2.6% (95% confidence interval, 1.8-3.3%) for each year decline in projected life expectancy. CONCLUSIONS: Life expectancy of a patient strongly influences evidence-based treatment in acute myocardial infarction. It was seen not only among patients with limited life expectancies but also among those with many years to live. Treatment care gaps may reflect clinicians' synthesis about frailty and life-expectancy gains.
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 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 | 0.003 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".