Incidence rates and contemporary trends in primary urethral cancer
Bibliographic record
Abstract
Abstract Purpose: We assessed contemporary incidence rates and trends of primary urethral cancer. Methods: We identified urethral cancer patients within Surveillance, Epidemiology and End Results registry (SEER, 2004–2016). Age-standardized incidence rates per 1,000,000 (ASR) were calculated. Log linear regression analyses were used to compute average annual percent change (AAPC). Results: From 2004–2016, 1,907 patients with urethral cancer were diagnosed (ASR 1.69; AAPC: -0.98%, p = 0.3). ASR rates were higher in males than in females (2.70 vs. 0.55), respectively and did not change over the time (both p = 0.3). Highest incidence rates were recorded in respectively ≥ 75 (0.77), 55–74 (0.71) and ≤ 54 (0.19) years of age categories, in that order. African Americans exhibited highest incidence rate (3.33) followed by Caucasians (1.72), other race groups (1.57) and Hispanics (1.57), in that order. A significant decrease occurred over time in Hispanics, but not in other race groups. In African Americans, male and female sex-stratified incidence rates were higher than in any other race group. Urothelial histological subtype exhibited highest incidence rate (0.92), followed by squamous cell carcinoma (0.41), adenocarcinoma (0.29) and other histologies (0.20). In stage stratified analyses, T1N0M0 stage exhibited highest incidence rate. However, it decreased over time (-3.00%, p = 0.02) in favor of T1 − 4N1 − 2M0 stage (+ 2.11%, p = 0.02). Conclusion: Urethral cancer is rare. Its incidence rates are highest in males, elderly patients, African Americans and in urothelial histological subtype. Most incident cases are T1N0M0, but over time, the incidence of T1N0M0 decreased in favor of T1 − 4N1 − 2M0.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".