P.142 Spinal cord injury associated with Wilms tumor metastasis: case report and literature review
Bibliographic record
Abstract
Background: A 19-month-old boy with recent Wilms tumor resection presented with ASIA B spinal cord injury secondary to rapid progression of a T12 epidural lesion suspicious for metastatic disease. Methods: The case is presented and the literature was reviewed for prior cases of Wilms tumor with spinal metastasis. Results: Emergent tumor debulking for spinal cord decompression via T11-L1 laminectomies with right T11-L1 facetectomies was performed. Allograft bone was placed to facilitate fusion. No direct connection to the renal tumor was appreciated on imaging or intra-operatively. There was no evidence of additional metastases. Pathology demonstrated similar histomorphology and immunohistochemical profile as the original left kidney tumor resection (Wilms tumor, favorable histology). Notably, no focal or diffuse anaplasia, or aggressive non-Wilms component such as clear cell sarcoma or rhabdoid tumor of the kidney, were identified. Treatment plan consisted of 25 Gy of radiation and 29 weeks of chemotherapy. At 6-weeks the patient had regained baseline lower extremity function with no bowel or bladder dysfunction. Conclusions: Spinal cord compression secondary to spinal metastasis of Wilms tumor in the absence of global metastatic disease is rare. Prompt identification, surgical decompression, and multimodality therapy is essential to prevent persistent neurological deficits.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".