The Impact of Under Coding of Cardiac Severity and Comorbid Diseases on the Accuracy of Hospital Report Cards
Bibliographic record
Abstract
CONTEXT: Hospital report cards usually are based on administrative discharge abstracts. However, cardiac severity and comorbidities generally are under-reported in administrative data. OBJECTIVE: We sought to determine how undercoding of cardiac severity and comorbidities affects the determination that some hospitals are high-mortality outliers. DESIGN: Simulations using retrospective data on 18,795 patients admitted with an acute myocardial infarction (AMI) to 109 acute care hospitals in Ontario. MAIN OUTCOME MEASURE: Change in the number of hospitals that remained high-mortality outliers after adjusting for potentially increased prevalence of as many as 9 separate measures of cardiac severity and comorbid conditions, individually or together. RESULTS: For most measures of cardiac severity and comorbidities, increasing the prevalence of each factor to the highest observed hospital-specific prevalence seldom altered the status of high-mortality outlier hospitals. Increases in the prevalence of cardiogenic shock or acute renal failure to even the median level led to reclassification of up to 4 of the 12 high-mortality outlier hospitals to nonoutlier status. Most high-mortality outlier hospitals were reclassified if the maximum prevalence was imputed for these 2 factors. Simultaneously increasing the prevalence of all comorbidities to the median level typically converted the status of about half the outlier hospitals. Not until the prevalence of all measures of cardiac severity and comorbidities were simultaneously increased to the maximum observed hospital-specific prevalence, did all hospitals initially classified as high-mortality outliers revert to nonoutlier status. CONCLUSIONS: Undercoding of severity and comorbidities in administrative data in itself is very unlikely to account for the outlier status of most hospitals. However, some potential for misclassification of individual institutions exists if influential factors are variably coded.
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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 | 0.092 | 0.433 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".