P6432The impact of patterns of long-term conditions on survival among patients with acute myocardial infarction: a national cohort study of 693,333 patients
Bibliographic record
Abstract
Background: The majority of patients with cardiovascular disease have at least one long-term condition (LTC). However, there is a paucity of large scale data on the extent, complexity, and impact of LTCs on survival among patients with acute myocardial infarction (AMI). Purpose: To determine multi-dimensional disease patterns of pre-existing LTCs for patients with AMI, and their association with survival. Methods: Patients with AMI with either none, one or two or more LTCs (including diabetes, COPD, heart failure, chronic renal failure, cerebrovascular disease and peripheral vascular disease) were identified in the Myocardial Ischaemia National Audit Project (2003–2013). Latent class analyses was used to assimilate individual patient data of multiple LTCs into classes representing the complex patterns of high order interactions between multiple LTCs observed amongst patients. All-cause mortality (up to 8.4 years, final follow up 30th December 2013) was estimated using flexible parametric survival models for individual and cumulative LTCs as well as latent class structures, whilst adjusting for patient demographic variables, patient clinical risk, pharmacological therapies and invasive coronary strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".