Development and validation of a prediction model for kidney failure in long-term survivors of childhood cancer: A report from the Childhood Cancer Survivor Study (CCSS).
Bibliographic record
Abstract
10047 Background: Kidney failure (need for dialysis or kidney transplantation, or death due to kidney disease) is a rare but serious late effect for survivors of childhood cancer. We aimed to develop a model using demographic and treatment characteristics to predict individual risk of kidney failure among five-year survivors of childhood cancer. Methods: CCSS survivors without kidney failure at five years after cancer diagnosis (n = 25,483) were assessed for subsequent kidney failure by age 40. Outcomes were self-reported and corroborated by the Organ Procurement and Transplantation Network and the National Death Index. A sibling cohort (n = 5045) served as a comparator. Piecewise exponential models with backward selection estimated the relationships between potential predictors and kidney failure and were converted to integer risk scores. Additional results from the St. Jude Lifetime Cohort Study (SJLIFE, n = 2490) and the National Wilms Tumor Study (NWTS, n = 6760) validated the models. Results: Among CCSS survivors, 204 developed late kidney failure. We developed a model with sex, race/ethnicity, age at cancer diagnosis, nephrectomy, exposure to specific chemotherapy, any abdominal radiation, presence of genitourinary anomalies, and early-onset hypertension (Table). Risk scores achieved an area under the curve (AUC) and concordance (C) statistic of 0.65 and 0.68 for kidney failure by age 40. Validation cohort AUC and C statistics were 0.83/0.86 for SJLIFE (8 cases) and 0.61/0.63 for NWTS (91 cases). An alternative model with specific chemotherapy doses and kidney-specific radiation dosimetry had similar AUC and C statistic (0.67/0.70). Integer risk scores were collapsed to form statistically distinct low (score <3; 87 cases of 17,326), moderate (score 3-5; 63 cases of 4667), and high (score 6+; 18 cases of 401) risk groups. These groups corresponded to cumulative incidences in CCSS of kidney failure by age 40 of 0.6% (95% CI 0.4-0.7%), 2.3% (95% CI 1.6-3.2%), and 9.4% (95% CI 4.4-16.7%), compared with 0.2% (95% CI 0.1-0.5%) among siblings. Conclusions: Using readily available information, we were able to identify low, moderate, and high risk groups for developing kidney failure following treatment for childhood cancer. These prediction models may help guide screening and interventional strategies for higher risk survivors.[Table: see text]
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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 | 0.018 | 0.022 |
| 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 | 0.001 | 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".