Contemporary conditional cancer‐specific survival rates in surgically treated nonmetastatic primary urethral carcinoma
Bibliographic record
Abstract
Abstract Background We examined the effect of disease‐free interval (DFI) duration on cancer‐specific mortality (CSM)‐free survival, otherwise known as the effect of conditional survival, in radical urethrectomy nonmetastatic primary urethral carcinoma (PUC) patients. Methods Using the Surveillance, Epidemiology, and End Results (SEER) database 2000–2020, patient (age, sex, race/ethnicity, and marital status) and tumor (stage and histology) characteristics, as well as systemic therapy exposure status of nonmetastatic PUC patients were tabulated. Conditional survival estimates at 5‐year were assessed based on DFI duration and according to stage at presentation (T1 –2N0 vs. T3–4N0–2). Results Of all 512 radical urethrectomy PUC patients, 278 (54%) harbored T1–2N0 stage versus 234 (46%) harbored T3–4N0–2 stage. In 512 PUC patients, 5‐year CSM‐free survival at initial diagnosis was 61.8%. Provided a DFI duration of 36 months, 5‐year CSM‐free survival was 85.6%. In 278 T1–2N0 PUC patients, 5‐year CSM‐free survival at initial diagnosis was 68.4%. Provided a DFI duration of 36 months, 5‐year CSM‐free survival was 86.9%. In 234 T3–4N0–2 PUC patients, 5‐year CSM‐free survival at initial diagnosis was 53.8%. Provided a DFI duration of 36 months, 5‐year CSM‐free survival was 83.6%. Conclusions Although intuitively, clinicians and patients are well aware of the concept that increasing DFI duration improves survival probability, only a few clinicians can accurately estimate the magnitude of survival improvement, as was done within the current study. Such information is crucial to survivors, especially in those diagnosed with rare malignancies, where the survival estimation according to DFI duration is even more challenging.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".