Factoring Socioeconomic Status Into Cardiac Performance Profiling for Hospitals
Bibliographic record
Abstract
BACKGROUND: Critics of "scorecard medicine" often highlight the incompleteness of risk-adjustment methods used when accounting for baseline patient differences. Although socioeconomic status is a highly important determinant of adverse outcome for patients admitted to the hospital with acute myocardial infarction, it has not been used in most risk-adjustment models for cardiovascular report cards. OBJECTIVES: To determine the incremental impact of socioeconomic status adjustments on age, sex, and illness severity for hospital-specific 30-day mortality rates after acute myocardial infarction. METHODS: The authors compared the absolute and relative hospital-specific 30-day acute myocardial infarction mortality rates in 169 hospitals throughout Ontario between April 1, 1994 and March 31, 1997. Patient socioeconomic status was characterized by median neighborhood income using postal codes and 1996 Canadian census data. They examined two risk-adjustment models: the first adjusted for age, sex, and illness severity (standard), whereas the second adjusted for age, sex, illness severity, and median neighborhood income level (socioeconomic status). RESULTS: There was an extremely strong correlation between 'standard' and 'socioeconomic status' risk-adjusted mortality rates (r = 0.99). Absolute differences in 30-day risk-adjusted mortality rates between the socioeconomic status and standard risk-adjustment models were small (median, 0.1%; 25th-75th percentile, 0.1-0.2). The agreement in the quintile rankings of hospitals between the socioeconomic status and standard risk-adjustment models was high (weighted kappa = 0.93). CONCLUSION: Despite its importance as a determinant of patient outcomes, the effect of socioeconomic status on hospital-specific mortality rates over and above standard risk-adjustment methods for acute myocardial infarction hospital profiling in Ontario was negligible.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".