© 2010 Canadian Medical Association or its licensors
Bibliographic record
Abstract
As the population grows older, the burden of cardio-vascular disease is increasing rapidly. In spite ofthe larger numbers of very elderly patients with coronary syndromes, many questions about treatment and its effects on outcomes remain unanswered. The rapidly evolving management of patients with acute coronary syndromes over the past decades has led to improved survival rates.1–5 However, these improvements have been observed mainly among the younger segment of the popula-tion.6 The use of invasive procedures is increasing over time, but data are conflicting regarding the relative increase and effectiveness of these procedures in the very elderly popula-tion.3,4,7 Recent studies comparing early invasive and conserva-tive strategies in patients with non-ST-segment elevation acute coronary syndromes have suggested that the benefit of inva-sive care was greater among the oldest (aged 75 years or older) patients.8–11 Whether the results of these studies published at the beginning of the present decade led to substantial changes in practice in care of the very elderly population is unknown. The impact on long-term outcomes of these likely changes in patterns of practice is also unknown. Therefore, our objectives were to describe the temporal trends, over a decade, in use of invasive cardiac procedures and prescribing of medications after acute myocardial infarction in a population of patients aged 80 years old and over. We aimed to describe the changes in risk profiles of these patients and to describe temporal changes in short- and long-term outcomes.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.831 | 0.686 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".