A Case of Primary Ewing Sarcoma of the Kidney: Robotic-Assisted Nephron-Sparing Surgery, a Feasible Alternative in Treatment of Localized Disease
Bibliographic record
Abstract
Extra-skeletal Ewing sarcoma (EWS) occurs in about 12% of EWS patients; at the same time, primary involvement of the kidneys remains extremely rare. Since it was first described in 1975, only a small case series have been reported worldwide. About 95% of surgically treated patients with EWS of the kidney described in the literature underwent nephrectomy, and the remaining patients only had a tumor biopsy. Nephron-sparing surgery (NSS) has not been sufficiently investigated as an alternative in the local surgical treatment of localized disease, mostly as a result of technically unfeasible provisions of negative surgical margins. In this report, we present a unique case of primary EWS of the kidney with an asymptomatic course without radiographic signs that suggest a highly aggressive disease, successfully locally treated with robotic-assisted NSS. This report showcases that robotic-assisted NSS could be a feasible alternative in treatment of localized disease yielding equally good oncological results while, at the same time, creating better prerequisites for necessary adjuvant chemotherapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".