Short-term and long-term survival in patients with prevalent haemodialysis—an integrated prognostic model: external validation
Bibliographic record
Abstract
OBJECTIVES: Prognostic tools with evidence for external validity in routine clinical practice are needed to align care with patients' preferences and deliver timely supportive services. Current models have limited, if any, evidence for external validity and none have been implemented and evaluated in clinical practice on a large scale. This study sought to provide evidence for external validity in a real life setting of the Cohen prognostic model that integrates actuarial factors with the 'Surprise Question' to assess 6-month, 12-month and 18-month survival of prevalent haemodialysis patients. METHODS: Cross-sectional study of 1372 patients in a Canadian university-based programme between 2010 and 2019. Survival probabilities were compared with observed survival. Discrimination and calibration were assessed through predicted risk-stratified observed survival, cumulative AUC, Somer's Dxy and a calibration slope estimate. RESULTS: Discrimination performance was moderate with a C statistic of 0.71-0.72 for all three time points. The model overpredicted mortality risk with the best predictive accuracy for 6- month survival. The differences between observed and mean predicted survival at 6 months, 12 months and 18 months were 3.2%, 8.8% and 12.9%, respectively. Kaplan-Meier curves stratified by Cox-based risk group showed good discrimination between high-risk and low-risk patients with HR estimates (95% CI): C2 vs C1 3.07 (1.57-5.99), C3 vs C1 5.85 (3.06-11.17), C4 vs C1 13.24 (6.91-25.34)). CONCLUSIONS: The Cohen prognostic model can be incorporated easily into routine dialysis care to identify patients at high risk for death over 6 months, 12 months and 18 months and help target vulnerable patients for timely supportive care interventions.
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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 | 0.027 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".