Primary lymphomas of the genitourinary tract: A population-based study
Bibliographic record
Abstract
We performed a population-based analysis focusing on primary extranodal lymphoma of either testis, kidney, bladder or prostate (PGUL). We identified all cases of localized testis, renal, bladder and prostate primary lymphomas (PL) versus primary testis, kidney, bladder and prostate cancers within the Surveillance, Epidemiology, and End Results database (1998–2015). Estimated annual proportion change methodology (EAPC), multivariable logistic regression models, cumulative incidence plots and multivariable competing risks regression models were used. The rates of testis-PL, renal-PL, bladder-PL and prostate-PL were 3.04%, 0.22%, 0.18% and 0.01%, respectively. Patients with PGUL were older and more frequently Caucasian. Annual rates significantly decreased for renal-PL (EAPC: −5.6%; p=0.004) and prostate-PL (EAPC: −3.6%; p=0.03). In multivariable logistic regression models, older ager independently predicted testis-PL (odds ratio [OR]: 16.4; p<0.001) and renal-PL (OR: 3.5; p<0.001), while female gender independently predicted bladder-PL (OR: 5.5; p<0.001). In surgically treated patients, cumulative incidence plots showed significantly higher 10-year cancer-specific mortality (CSM) rates for testis-PL, renal-PL and prostate-PL versus their primary genitourinary tumors. In multivariable competing risks regression models, only testis-PL (hazard ratio [HR]: 16.7; p<0.001) and renal-PL (HR: 2.52; p<0.001) independently predicted higher CSM rates. PGUL rates are extremely low and on the decrease in kidney and prostate but stable in testis and bladder. Relative to primary genitourinary tumors, PGUL are associated with worse CSM for testis-PL and renal-PL but not for bladder-PL and prostate-PL, even after adjustment for other-cause mortality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".