Extent of Screening for CKD Development and Monitoring for CKD Complications in Childhood Cancer Survivors: A Single-Centre Retrospective Cohort Study
Bibliographic record
Abstract
Background: Childhood cancer survivors (CCS) are at risk for CKD. The extent to which CCS are screened for CKD, or monitored for CKD complications is unknown. We evaluated: 1) the extent of screening for CKD in CCS ≥6 months post-cancer therapy; 2) in patients attaining CKD criteria, the extent of monitoring for CKD complications and of nephrology referral; and 3) the association of patient and cancer characteristics with CKD screening. Methods: Retrospective cohort study of CCS, ≤18 years at cancer diagnosis from 2016–2020 at a quaternary care referral institution. Outcomes: 1) CKD screening (serum creatinine [SCr] or proteinuria); 2) CKD complications monitoring (i.e., repeat SCr/proteinuria; nephrology referral; vitamin D; PTH; hemoglobin; frequency per KDIGO guideline/CKD severity). CKD defined as: a) “non-strict”: any abnormal result; b) “strict”: ≥2 abnormal results (eGFR or proteinuria); ≥3 months apart; no normal results in between. Associations between characteristics and CKD screening were evaluated using distribution-appropriate univariable analyses. Results: Of 443 CCS, only 240 (54.2%) and 132 (29.8%) were screened for non-strict and strict CKD, respectively; 41 (17.1%) and 15 (11.4%) attained criteria for non-strict and strict CKD, respectively. In CCS with non-strict and strict CKD respectively, percentages with complications monitoring: SCr measured (92.7%; 100.0%); urine protein measured (76.9%; 86.7%); nephrology referral (54.1%; 61.5%); vitamin D (36.8%; 61.5%); PTH (31.6%; 38.5%); hemoglobin (90.2%; 93.3%). Table: several variables were associated with CKD screening; many are recognized kidney risk factors. Conclusion: Screening for CKD and monitoring for CKD complications in CCS are not ideal. Kidney guidelines and follow-up in CCS should be improved to reduce CKD and complications. Funding: Government Support – Non-U.S.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".