Prognostic Factors of Survival for High-Grade Neuroendocrine Neoplasia of the Bladder: A SEER Database Analysis
Bibliographic record
Abstract
Background: High-grade neuroendocrine carcinoma (NEC) is a rare and aggressive variant of bladder cancer. Considering its rarity, its therapeutic management is challenging and not standardized. Methods: We analyzed data extracted from the Surveillance, Epidemiology, and End Results (SEER) registry to evaluate prognostic factors for high-grade NEC of the bladder. Results: We extracted data on 1134 patients: 77.6% were small cell NEC, 14.6% were NEC, 5.5% were mixed neuro-endocrine non-neuroendocrine neoplasia, and 2.3% were large cell NEC. The stage at diagnosis was localized for 45% of patients, lymph nodal disease (N+M0) for 9.2% of patients, and metastatic disease for 26.1% of patients. The median overall survival (OS) was 12 months. Multivariate analysis detected that factors associated with worse OS were age being >72 years old (HR 1.94), lymph nodal involvement (HR 2.01), metastatic disease (HR 2.04), and the size of the primary tumor being >44.5 mm (HR 1.80). In the N0M0 populations, the size of the primary tumor being <44.5 mm, age being <72 years old, and major surgery were independently associated with a lower risk of death. In the N+M0 group, the size of the primary lesion was the only factor to retain an association with OS. Conclusions: Our SEER database analysis evidenced prognostic factors for high-grade NEC of the bladder that are of pivotal relevance to guide treatment and the decision-making process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.001 | 0.000 |
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".