Predicting the Risk of 1-Year Mortality in Incident Dialysis Patients
Bibliographic record
Abstract
BACKGROUND: Administrative databases are increasingly being used to study the incident dialysis population and have important advantages. However, traditional methods of risk adjustment have limitations in this patient population. OBJECTIVE: Our objective was to develop a prognostic index for 1-year mortality in incident dialysis patients using administrative data that was applicable to ambulatory patients, used objective definitions of candidate predictor variables, and was easily replicated in other environments. RESEARCH DESIGN: Anonymized, administrative health data housed at the Institute for Clinical Evaluative Sciences in Toronto, Canada were used to identify a population-based sample of 16,205 patients who initiated dialysis between July 1, 1998 and March 31, 2005. The cohort was divided into derivation, validation, and testing samples and 4 different strategies were used to derive candidate logistic regression models for 1-year mortality. The final risk prediction model was selected based on discriminatory ability (as measured by the c-statistic) and a risk prediction score was derived using methods adopted from the Framingham Heart Study. Calibration of the predictive model was assessed graphically. RESULTS: The risk of death during the first year of dialysis therapy was 16.4% in the derivation sample. The final model had a c-statistic of 0.765, 0.763, and 0.756 in the derivation, validation, and testing samples, respectively. Plots of actual versus predicted risk of death at 1-year showed good calibration. CONCLUSION: The prognostic index and summary risk score accurately predict 1-year mortality in incident dialysis patients and can be used for the purposes of risk adjustment.
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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 | 0.001 |
| 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 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".