Unwrapping Nephrogenic Rests and Nephroblastomatosis for Pediatric Surgeons: A Systematic Review Utilizing the PICO Model by the APSA Cancer Committee
Bibliographic record
Abstract
BACKGROUND: Nephrogenic rests (NR) may represent precursor lesions for Wilms tumor (WT), but their clinical course is not fully understood and no guidelines for treatment exist. This study sought to evaluate the outcomes of pediatric patients with NRs related to traditional chemotherapy and surgery. METHODS: A PRISMA-P-compliant literature search was conducted in MEDLINE, Embase, CINAHL, Web of Science, COCHRANE, and SCOPUS from inception to June 2021. Clinical questions regarding the treatment of NRs, including chemotherapy and surgery, were developed in the population, intervention, comparison, and outcome format. RESULTS: Twenty-five studies including 1445 patients met inclusion criteria for evaluating chemotherapy compared to observation for NRs. Eighteen studies including 1392 patients met inclusion criteria for evaluating the role of surgery for NRs. Patients with isolated NRs who underwent observation progressed to WT 33% of the time; chemotherapy reduced the rate of WT to 3.9%. Observation of multiple NRs and diffuse hyperplastic perilobar nephroblastomatosis (DHPLN) led to progression to WT 50% and 100% of the time, respectively. Chemotherapy reduced the rate of WT to 34% for multiple NRs and 46% for DHPLN. Surgery for isolated NRs reduced the risk of WT development from 23.7% in patients with incomplete excision to 3.3% with complete excision; however, 96% of patients with incompletely excised NRs had bilateral disease. CONCLUSIONS: Observation with close surveillance for isolated NRs is safe. Treatment with chemotherapy is recommended for patients with multiple NRs and DHPLN. Surgical management of NRs should focus on renal function preservation. LEVEL OF EVIDENCE: Treatment study, Level III.
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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 | 0.016 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".