Statins and superficial bladder cancer evolution after diagnosis: A population-based study
Bibliographic record
Abstract
22047 Background: Statins are mostly associated with diminished incidence of many solid cancers. Our objective is to determine if statins reduce the risk of superficial bladder cancer [SBC] evolution after diagnosis. Methods: This study was based on administrative databases in Quebec, Canada, containing data on patient demography, prescription claims and physician services. Study patients were newly diagnosed with SBC and had a trans-urethral resection of tumor [TUR-BT] procedure between July 1, 1995 and January 1, 2002. We excluded patients with prior cancer diagnosis and those with immediate cystectomy, chemotherapy or radiation therapy. All eligible patients were grouped into cohorts of statin users and non-users. Cox regression models assessed time to recurrence, to progression to cystectomy and to death from all causes. Co-variables included were: sex, age, intravesical adjuvant therapy, chronic disease score, medical and emergency visits, and hospitalization days in the year prior to first TUR-BT. Models were adjusted for immortal time and used time dependent classification of exposure to statins. Results: Of the 4,834 cohort patients, 1,211 (25%) were statin users. Recurrence, progression of SBC, and death were observed in 2,340 (48%), 225 (5%), and 1,051 (22%) of patients, respectively. Baseline variables were similar in statin users and non users, except for intravesical adjuvant therapy, more frequent in unexposed patients (9.9% vs 7.8%). Statin use was associated with a Hazard Ratio [HR] (95% CI) of 0.97 (0.86–1.09) for recurrence, a HR of 0.58 (0.38–0.91) for SBC progression to cystectomy and a HR of 0.54 (0.44–0.66) for death from all causes. Conclusions: Statin use does not seem to have an impact of bladder cancer recurrence, but appears protective for superficial bladder cancer progression to muscle-invasive status leading to cystectomy, and for all-cause death. Its apparent efficacy suggests that further investigation in randomized trials may be warranted. No significant financial relationships to disclose.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".