Malignant peripheral nerve sheath tumor of kidney.
Bibliographic record
Abstract
A 40-year-old man presented with complaints of left-sided lower back discomfort for 2 to 3 months. There was no other significant history. Physical examination was unremarkable except for a palpable mass in the left flank. Ultrasonography showed a hypoechoic mass. Biphasic contrast-enhanced computerized tomogram revealed a large, heterogenous, infiltrating, necrotic mass lesion arising from the left kidney with blood supply from the left renal artery. Fine needle aspiration cytology showed sheaths, bundles and whorls of cells with indistinct cell margin, moderate amount of eosinophilic cytoplasm, elongated wavy nuclei and moderate anisocytosis suggestive of malignant peripheral nerve sheath tumor (× 100, hematoxylin-eosin). He underwent a radical tumor excision. Malignant peripheral nerve sheath tumor is derived from Schwann cells. The occurrence of its isolated form in the kidney capsule is extremely rare. Only 5 cases of malignant peripheral nerve sheath tumor of kidney are reported till date.1,2 Symptoms are insidious and nonspecific. Computed tomography can help us differentiating this unique histological character from common renal tumors with features of larger size, infiltrative nature, necrotic areas, and lack of extension into the renal vein. The differential diagnosis in our case included aggressive renal cell carcinoma, small round cell tumor, and sarcoma. The implication lies in the difference in treatment strategies depending on histology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".