Rare histological prostate cancer subtypes: Cancer-specific and other-cause mortality
Bibliographic record
Abstract
BACKGROUND: To assess cancer-specific mortality (CSM) and other-cause mortality (OCM) rates in patients with rare histological prostate cancer subtypes. METHODS: Using the Surveillance, Epidemiology, and End Results database (2004-2020), we applied smoothed cumulative incidence plots and competing risks regression (CRR) models. RESULTS: Of 827,549 patients, 1510 (0.18%) harbored ductal, 952 (0.12%) neuroendocrine, 462 (0.06%) mucinous, and 95 (0.01%) signet ring cell carcinoma. In the localized stage, five-year CSM vs. OCM rates ranged from 2 vs. 10% in acinar and 3 vs. 8% in mucinous, to 55 vs. 19% in neuroendocrine carcinoma patients. In the locally advanced stage, five-year CSM vs. OCM rates ranged from 5 vs. 6% in acinar, to 14 vs. 16% in ductal, and to 71 vs. 15% in neuroendocrine carcinoma patients. In the metastatic stage, five-year CSM vs. OCM rates ranged from 49 vs. 15% in signet ring cell and 56 vs. 16% in mucinous, to 63 vs. 9% in ductal and 85 vs. 12% in neuroendocrine carcinoma. In multivariable CRR, localized neuroendocrine (HR 3.09), locally advanced neuroendocrine (HR 9.66), locally advanced ductal (HR 2.26), and finally metastatic neuroendocrine carcinoma patients (HR 3.57; all p < 0.001) exhibited higher CSM rates relative to acinar adenocarcinoma patients. CONCLUSIONS: Compared to acinar adenocarcinoma, patients with neuroendocrine carcinoma of all stages and locally advanced ductal carcinoma exhibit higher CSM rates. Conversely, CSM rates of mucinous and signet ring cell adenocarcinoma do not differ from those of acinar adenocarcinoma.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".