Geography and service supply do not explain socioeconomic gradients in angiography use after acute myocardial infarction.
Bibliographic record
Abstract
BACKGROUND: Socioeconomic status appears to be an important predictor of coronary angiography use after acute myocardial infarction. One potential explanation for this is that patients with lower socioeconomic status live in neighbourhoods near nonteaching hospitals that have no catheterization capacity, few specialists and lower volumes of patients with acute myocardial infarction. This study was conducted to determine whether the impact of socioeconomic status on angiography use would be lessened by considering variations in the supply of services. METHODS: We examined payment claims for physician services, hospital discharge abstracts and vital status data for 47 036 patients with acute myocardial infarction admitted to hospitals in Ontario between April 1994 and March 1997. Neighbourhood income of each patient was obtained from Canada's 1996 census. Using multivariate hierarchical logistic regression and adjusting for baseline patient and physician factors, we examined the interaction among hospital and regional characteristics, socioeconomic status and angiography use in the first 90 days after admission to hospital for acute myocardial infarction. RESULTS: Within each hospital and geographic subgroup, crude rates of angiography rose progressively with increases in neighbourhood income. After adjusting for sociodemographic, clinical and physician characteristics, hospitals with on-site angiography capacity (adjusted odds ratio [OR] 1.88, 95% confidence interval [CI] 1.52-2.33), those with university affiliations (adjusted OR 1.60, 95% CI 1.27-2.01) and those closest to tertiary institutions (adjusted OR 1.57, 95% CI 1.32-1.87) were all associated with higher 90-day angiography use after acute myocardial infarction. However, the relative impact of socioeconomic status on 90-day angiography use was similar whether or not hospitals had on-site procedural capacity (interaction term p = 0.68), had university affiliations (interaction term p = 0.99), were near tertiary facilities (interaction term p = 0.67) or were in rural or urban regions (interaction term p = 0.90). INTERPRETATION: Socioeconomic status was as important a predictor of angiography use in hospitals with ready access to cardiac catheterization facilities as it was in those without. The socioeconomic gradient in the use of angiography after acute myocardial infarction cannot be explained by the distribution of specialists or tertiary hospitals.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".