Urinary Large Cell Neuroendocrine Carcinoma
Bibliographic record
Abstract
Large cell neuroendocrine carcinoma (LCNEC) of the urinary tract is a rare disease. We present a relatively large retrospective cohort of urinary LCNEC, 20 from the urinary bladder, and 2 from the ureter, from a single institution. The patients included 16 men and 6 women with a median age of 74.5 years. Most LCNEC presented at an advanced stage with tumors invading the muscularis propria and beyond (21/22). Eight cases were pure LCNEC, while 14 cases were mixed with other histologic types, including conventional urothelial carcinoma (n=9), carcinoma in situ (n=7), small cell carcinoma (n=6), and urothelial carcinoma with glandular (n=3) features. Most LCNEC expressed neuroendocrine markers synaptophysin (22/22), chromogranin (13/16), CD56 (7/7), TTF1 (8/8), and INSM1 (2/3). They were negative for common urothelial markers including HMWCK (0/3), p40/p63 (0/6), CK20 (0/10), and had variable GATA3 staining (4/8). Ki-67 stained 25% to nearly 100% tumor cell nuclei. Patient survival was associated with cancer stage, and pure LCNEC showed worse survival than mixed LCNEC. Compared with small cell carcinoma at similar stages from a prior study, LCNEC had a worse prognosis only when patients developed metastatic disease. For organ-confined LCNEC, neoadjuvant chemotherapy followed by radical resection is the treatment option to achieve long-term survival.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".