PS1 - 159 A Case of Intracranial Metastases from Renal Pelvic Carcinoma and Review of Literature
Bibliographic record
Abstract
Transitional cell carcinoma (TCC) of the renal pelvis is a rare urological malignancy, with only a handful of cases of metastases to the brain reported in literature. We aim to present a case of intracranial metastasis in a female patient with history of renal pelvic carcinoma, and review existing literature of brain metastases from renal pelvic TCC. Methods: We searched PubMed, EMBASE, and MEDLINE from 1966 to January 2016 for published case reports written in English. Results: Five published case reports describe intracranial metastases from renal pelvic TCC. Our case is a 56-year-old woman with known high grade renal pelvic carcinoma and pulmonary metastases, who presented nine years after her initial diagnosis with mild left side weakness and headaches. She was found to have two lesions in the right cerebral hemisphere and underwent surgical resection of the larger right frontal lobe mass. Her neurologic symptoms improved postoperatively. She declined whole brain radiotherapy and remains stable at 6 months’ follow-up. This is the first published case of presentation of brain metastases from pelvic TCC more than 12 months after diagnosis of the primary cancer. Conclusion: There is minimal literature of renal pelvic TCC metastasizing to the brain. However, as systemic chemotherapy leads to improved survival from the primary cancer, it is possible for more cases to appear, necessitating increased awareness from the healthcare team.
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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 | 0.006 | 0.005 |
| 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) | 0.002 | 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".