Sarcopenia in Children With Wilms Tumor: A Marker of Undernutrition Which May Impact Adversely on Clinical Outcomes
Bibliographic record
Abstract
The therapeutic approach to Wilms tumor (WT) is multidisciplinary and leads to significant patient impairment, increasing the risk of nutritional compromise and malnutrition. Children with cancer are vulnerable to sarcopenia which has been recognized as a negative impact of anticancer therapy. Recent studies have highlighted the reduction in the total psoas muscle area (TPMA) to be associated with a poor prognosis in many pediatric diseases, including cancer. This study aims to evaluate changes in the TPMA compartment during the treatment of children with WT. An observational, longitudinal, and retrospective study was undertaken in a single institution evaluating children (1 to 14 y, n=38) with WT between 2014 and 2020. TPMA was assessed by the analysis of previously collected, electronically stored computed tomography images of the abdomen obtained at 3 time points: diagnosis, preoperatively, and 1 year after surgery. For all patients, TPMA/age were calculated with a specific online calculator. Our data show a high incidence of sarcopenia (55.3%) at diagnosis which increased after 4 to 6 weeks of neoadjuvant chemotherapy (73.7%) and remained high (78.9%) 1 year after the surgical procedure. Using TPMA/age Z-score curves we have found significant and rapid muscle loss in children with WT, with little or no recovery in the study period.
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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 | 0.001 |
| 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 | 0.000 |
| 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".