External validation of a prognostic model in routine practice for short- and long-term survival in peritoneal dialysis
Bibliographic record
Abstract
BackgroundThere are several indices to predict survival at dialysis start but tools to predict mortality for prevalent patients are lacking. This study provides evidence for external validity of the Cohen model to assess 6-, 12-, and 18-months survival of prevalent peritoneal dialysis (PD) patients.MethodsProspective cohort study of 464 PD patients in a university-based program between 2015 and 2019. Survival probabilities were compared to observed survival. Discrimination and calibration were assessed through predicted risk-stratified observed survival, cumulative area under the curve, Somer's Dxy, and a calibration slope estimate.ResultsDiscrimination performance was moderate with c-statistic of 0.73 to 0.74 for all 3 time points. The model over predicted mortality risk with the best predictive accuracy for 6-month survival. The difference between observed and mean predicted survival at 6, 12, and 18 months was 3.1%, 5.5%, and 11.0%. Kaplan-Meier curves showed good discrimination between low- and high-risk patients with hazard ratios [95% confidence interval (CI)]: C4 vs C1 32.0 [4.3-236.5]. Miscalibration of the model was the greatest for the highest risk patient group in whom 12 and 18 months predicted survival was 15% and 28% lower than observed survival.ConclusionsThe Cohen prognostic model can identify PD patients at high risk for death over 6, 12, and 18 months. Given it overestimates mortality risk for the highest risk patients, care must be taken to not use predictions to withhold treatment but rather to risk stratify and identify those who may benefit from enhanced kidney supportive care. This miscalibration provides an imperative to refine the tool for PD patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".