P6433Association between optimal guideline-indicated care and survival in patients with acute myocardial infarction and long-term conditions: a population based cohort study
Bibliographic record
Abstract
Background: Long term conditions (LTC) are common in patients with acute myocardial infarction (AMI), however the effect of a LTC on the treatment patients receive has not been investigated. As treatment receipt is associated with better survival, we hypothesise that lower survival in patients with a LTC may be explained partly by reduced treatment. Purpose: To investigate the impact of LTCs for AMI patients on the receipt of guideline indicated care and the combined effect of LTC and receipt of care on survival. Methods: Data from the Myocardial Ischaemia National Audit Project (MINAP, 2003–2013) were used to investigate 693,388 patients with ST-elevation myocardial infarction (n=274,220) and non-ST-elevation myocardial infarction (n=419,168). Receipt of care was determined by proportion of eligible care components received by patients according to international guidelines and optimal vs. suboptimal care defined as receiving all care opportunities vs. missing one or more care opportunity. Poisson models were fitted to determine the association between LTCs (including diabetes, heart failure, renal failure, COPD, peripheral vascular disease, and cerebrovascular disease) and the number of care components received, whilst binomial models investigated the odds of receiving optimal care with or without a LTC. Flexible parametric survival models were fitted to determine the interacting effect of LTCs and optimal care on survival.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".