Prediction model for 6-month mortality in incident older hemodialysis patients in South Korea
Bibliographic record
Abstract
BACKGROUND: Early mortality following hemodialysis initiation hinders survival improvement in older patients. This study aimed to develop a clinical risk model for predicting 6-month mortality after dialysis initiation in older Korean hemodialysis patients. METHODS: We analyzed data from incident hemodialysis patients aged >70 years from the Korean Society of Geriatric Nephrology (KSGN) database. A prediction model was developed using multivariate logistic regression analysis and externally validated with independent datasets. RESULTS: Among 1,751 incident hemodialysis patients, the 6-month mortality rate was 15.5%. Using multivariate logistic analysis, we constructed the KSGN score as an independent risk factor for 6-month mortality, and its components and score are as follows: old age at dialysis initiation (≥85 years, score 2); hypertension and renovascular disease as a primary etiology of end-stage kidney disease (ESKD) (score 1); malignancy history (yes, score 1); low serum albumin (<3.5 g/dL, score 1); hypertension treatment (yes, score -1); prepared vascular access on maintenance dialysis (arteriovenous fistula/arteriovenous graft, score -3). In the development cohort, the area under the curve (AUC) for the KSGN score was significantly higher than the Alberta Wick's score (0.707 vs. 0.683, p = 0.001). In the validation cohort, the KSGN score's performance was comparable to existing models. CONCLUSION: The KSGN score may be a valuable tool for predicting early mortality after dialysis initiation in older patients with ESKD, aiding in decision-making and management regarding dialysis initiation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".