Using the Johns Hopkins Aggregated Diagnosis Groups (ADGs) to Predict Mortality in a General Adult Population Cohort in Ontario, Canada
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Administrative healthcare databases are increasingly used for health services and comparative effectiveness research. When comparing outcomes between different treatments, interventions, or exposures, the ability to adjust for differences in the risk of the outcome occurring between treatment groups is important. Similarly, when conducting healthcare provider profiling, adequate risk-adjustment is necessary for conclusions about provider performance to be valid. There are limited validated methods for risk adjustment in ambulatory populations using administrative healthcare databases. OBJECTIVES: To examine the ability of the Johns Hopkins' Aggregated Diagnosis Groups (ADGs) to predict mortality in a general ambulatory population cohort. RESEARCH DESIGN: Retrospective cohort constructed using population-based administrative data. SUBJECTS: All 10,498,413 residents of Ontario, Canada between the ages of 20 and 100 years who were alive on their birthday in 2007. Subjects were randomly divided into derivation and validation samples. MEASURES: Death within 1 year of the subject's birthday in 2007. RESULTS: A logistic regression model consisting of age, sex, and indicator variables for 28 of the 32 ADG categories had excellent discrimination: the c-statistic (equivalent to the area under the receiver operating characteristic curve) was 0.917 in both derivation and validation samples. Furthermore, the model showed very good calibration. In comparison, the use of the Charlson comorbidity index or the Elixhauser comorbidities resulted in a minor decrease in discrimination compared with the use of the ADGs. CONCLUSIONS: Logistic regression models using age, sex, and the John Hopkins ADGs were able to accurately predict 1-year mortality in a general ambulatory population of subjects.
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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 | 0.000 | 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 it