Risk Factors for Long-Term Kidney Outcomes in Childhood Cancer Survivors
Bibliographic record
Abstract
Background: Childhood cancer survivor (CCS) follow-up guidelines are unclear on how to risk-stratify for and ascertain long-term kidney outcomes (KO). We evaluated the relation of patient/treatment factors with KO in CCS up to 4 years post-therapy. Methods: Prospective, national data (secondary use: Cancer in Young People in Canada database; CCS, ≤15-years-old at cancer diagnosis post Jan 1, 2001; 17 centers). Excluded: no birth date/sex; died on treatment. Outcome: KO (included hypertension, nephritis, Fanconi syndrome, CKD, AKI, fluid retention, kidney atrophy, high creatinine, low GFR, kidney stone/abscess, thrombotic microangiopathy; selection adjudicated by two authors). Univariable and multivariate logistic regression was used to evaluate independent risk factors for KO. Results: 18,065 CCS included (178 with KO [1%] vs. 17,818 no KO). Age(p=0.42), sex(p=0.96), ethnicity(p=0.41), income quintile(p=0.46), main cancer diagnosis(p<0.001), abdominal radiation during therapy p=0.66), graft vs. host disease (GVHD) (p<0.004), number of hematopoietic stem cell transplants (HSCT)(p<0.001), center geography(p<0.001) and home distance from center(p=0.54) were evaluated in univariable analyses for association with KO. Adjusted analyses(Fig): More HSCT and kidney or hepatic tumor (vs. leukemia/lymphoma) were associated with higher adjusted odds of KO; East/West Canada (vs. Central) and other cancer diagnoses (shown, Fig) were associated with lower adjusted odds of KO. Conclusions: Number of HSCT and cancer type are associated with KO in CCS. KO recording is suboptimal, speaking to lack of awareness and unclear CCS kidney health guidelines. We will use our data to initiate knowledge translation with child oncology stakeholders, explore barriers/facilitators to kidney health monitoring and improve current follow-up guidelines.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".