Clinical Outcomes and Prognosis Analysis of Younger Bladder Cancer Patients
Bibliographic record
Abstract
BACKGROUND: Generally, little is known about prognostic factors in bladder cancer patients under 40 years of age. We therefore performed a retrospective study to identify prognostic factors in these younger bladder cancer patients. METHODS: We collected clinicopathological data on bladder cancer patients ≤40 years old diagnosed between 1975 and 2018 from the Surveillance, Epidemiology, and End Results (SEER) database. Survival curves were generated using the Kaplan-Meier method, and the differences between groups were analyzed using the log-rank test. Univariate and multivariate Cox hazards regression analyses were performed to define hazard ratios (HRs) for cancer-specific survival (CSS). RESULTS: There were statistical differences in race, histological type, cancer stage, tumor size, and surgery treatment groups between overall survival and CSS. Only tumor size and cancer stage were significant independent prognostic risk factors in younger bladder cancer patients for the prediction of CSS. CONCLUSION: Tumors greater than 30 mm in size and a more advanced stage of bladder cancer were indicative of a poor prognosis in bladder cancer patients ≤40 years old, and long-term follow-up is suggested.
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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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".