Surgical challenges in the treatment of a giant renal cell carcinoma with atypical presentation
Bibliographic record
Abstract
INTRODUCTION: For the management of localized renal cell carcinoma (RCC), surgical resection is the standard of care. Considerations are given to achieve good outcomes with conservative measures. When the tumor is exceedingly large the safest alternative is total nephrectomy. PRESENTATION OF CASE: The patient is a 75year old man with a 5year history of increasing abdominal distension. There was no recent hematuria or any other genitourinary complaints. CT revealed a giant complex mass that occupied the majority of the abdomen likely arising from the retroperitoneum. Early in diagnosis, the mass was suspected to arise from the left kidney. The decision was made to proceed with surgery for both treatment and diagnosis. Resection of the tumor revealed a 28.0×25.0×15.0cm encapsulated neoplasm. Histopathology determined this to be a papillary RCC. Resection of the mass resulted in en bloc partial nephrectomy immediately followed by a completion of the nephrectomy, lymphadenectomy, and abdominal wall repair. Postoperative course was excellent. DISCUSSION: The aim of this report is to determine the surgical challenges posed by a tumor of this magnitude and the multidisciplinary approach necessary to treat it. In the often indolent course seen with RCC, surgeons are faced with the task of handling advanced disease, requiring more radical procedures for good outcomes. CONCLUSION: The size of the tumor in this case presented several challenges in the operative setting. The sheer mass of the tumor gave no other choice than to perform exploratory laparotomy and complete nephrectomy upon resection.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".