Prediction Model for 6-Month Mortality in Incident Older Hemodialysis Patients: Data From the Korean Society of Geriatric Nephrology
Bibliographic record
Abstract
Background: Early mortality following hemodialysis initiation is a barrier to improving patient survival. We aimed to develop a clinical risk model to predict the early mortality of older hemodialysis patients. Methods: Hemodialysis patients aged >70 years were recruited from a retrospective cohort from the Korean Society of Geriatric Nephrology (KSGN). A prognostic score for 6-month mortality risk after dialysis initiation was developed, named the KSGN score. Multivariate Cox regression analysis was used to select risk factors from 20 clinical variables. β-coefficients were converted to natural logarithms for the final risk score model. Results: Among the 1,967 incident hemodialysis patients, the crude 6-month mortality rate was 15.7% (n=309). In the multivariate Cox analysis, independent risk factors for 6-month mortality and each score were as follows: the body mass index (<18.5 kg/m2 (0), 18.5≤, <23 kg/m2 (0)), age at dialysis initiation (<80 years (0), ≥85 years (1)), status of malignancy (curative state (0), palliative treatment (1)), hypertension (0), nursing hospital care at dialysis initiation (0), vascular access at dialysis initiation (arteriovenous graft (-1)), vascular access on maintenance dialysis (arteriovenous fistula (-1), arteriovenous graft (-1)), and serum albumin (0)). According to the KSGN score, mortality rate was 4.8%, 8.6%, 32.0%, 60.3%, and 66.7% for -2, -1, 0, 1, and 2 points, respectively. The area under the curve of the KSGN score was significantly higher than that of either the Alberta or United States Renal Data System scores. Conclusions: The KSGN score is a simple tool to predict early mortality after dialysis initiation in older patients with end-stage kidney disease and may be useful to support decision-making and management in older adults starting dialysis.Comparison of ROC curve between the prognostic models
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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 | 0.003 | 0.006 |
| 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 | 0.000 | 0.000 |
| 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".