Body Mass Index as a Risk Factor for Kidney Health in Childhood Cancer Survivors
Bibliographic record
Abstract
Background: Childhood Cancer (CC) survivors are at risk for kidney outcomes(KO). High Body Mass Index (BMI) is a risk factor for kidney disease. We evaluated the relation between BMI in CC therapy with KO. Methods: Secondary use of national prospective data(Cancer in Young People in Canada database: ≤15-yrs-old diagnosed with cancer at 17 Canadian centers after Jan 1, 2001; followed up to 6 yrs). Excluded: no birth date/sex; died during therapy; no BMI. Exposures: first BMI(within 6 mths of diagnosis); last BMI; last vs. first percent change BMI percentile. Outcomes: any kidney or BP outcome after CC therapy. Multivariate logistic regression used to evaluate exposure-KO relations, adjusted for location, cancer diagnosis, graft vs. host and number of hematopoietic transplants. Results: 9805 children included; n=90(0.92%) had KO recorded. Patients with vs. without KO had higher first BMI, lower last BMI and BMI %change (only significant for first BMI, Fig 1). Adjusted analyses: first BMI was associated with higher adjusted odds for post-therapy KO(adjusted OR 1.01 [1.00-1.02], p<0.02, Table 1).Figure 1:: First, Last and %Change in BMI Percentile stratified by Kidney Outcome statusConclusions: Higher adiposity early in CC therapy is associated with kidney and BP conditions. It is unclear if this is due to higher risk of recurrent AKI or underlying longterm risk. Children with high BMI at CC therapy start may be targeted for closer kidney health follow-up post-therapy.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".