Secondary bladder and colorectal cancer after treatments for prostate cancer: A population based study
Bibliographic record
Abstract
BACKGROUND: Prostate cancer (PCa) patients receiving radiotherapy may be predisposed to secondary malignancies. This study aimed to determine the association between PCa treatments, including radical prostatectomy (RP), external beam radiation therapy (EBRT), brachytherapy (BT) and androgen deprivation therapy (ADT); and secondary bladder and colorectal cancer. METHODS: A cohort study was constructed using Quebec administrative databases (Med-Echo and RAMQ). Included men were diagnosed and treated for PCa between 2000 and 2016. Patients with bladder or colorectal cancer prior to PCa were excluded. Follow-up ended at the earliest of the following: incidence of bladder or colorectal cancer, death, or December 31, 2016. EBRT, BT, EBRT+ADT, RP + ADT or ADT only were compared individually to RP. The incidence of secondary bladder and colorectal cancer were computed. Inverse probability of treatment weighting (IPTW) based on a propensity score was used to control for potential confounding. IPTW-Cox proportional hazards models were used. RESULTS: A significant association was found between secondary bladder cancer and EBRT (HR: 1.84, 95%CI: 1.60;2.13), and also EBRT+ADT (HR: 2.08, 95%CI: 1.67;2.56), but not with BT (HR: 1.36, 95%CI: 0.68;2.74). Secondary colorectal cancer was significantly associated to either EBRT (HR: 1.36, 95%CI: 1.21;1.53); or BT (HR: 2.46, 95%CI: 1.71;3.54). The association between ADT alone and both secondary cancers was also significant (HR: 1.98, 95%CI: 1.69;2.31 and HR: 1.69, 95%CI: 1.49;1.92, respectively). CONCLUSIONS: Compared to PCa patients undergoing RP, the secondary bladder cancer was associated with EBRT, ADT, alone or in combination. The secondary colorectal cancer was also associated with receiving either EBRT, BT or ADT.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".