Bibliographic record
Abstract
Bladder cancer kills more women than cervical cancer and is also a significant cause of mortality in men. Little progress has been made in improving survival in patients with advanced bladder cancer. Two pilot studies using microhematuria screening have shown that screening for bladder cancer results in close to 80% downstaging, with high-grade cancers being detected before they have invaded the bladder wall. Results of long-term follow-up even suggest a striking reduction in bladder cancer mortality. The main obstacles to screening for bladder cancer may be overcome if a higher-risk population is identified by designing a risk scale for exposure to cigarette smoke and occupational carcinogens, and through genetic testing for susceptibility to cancer and home hematuria screening, which in itself identifies a population with approximately 3% to 4% risk of bladder cancer. The feasibility and cost effectiveness of screening for bladder cancer can be significantly improved by incorporating a secondary screening strategy using a more sensitive and specific bladder cancer marker that is currently available, and by limiting urological evaluations to patients who show positive results on one or more of these tests. Bladder cancer is the most costly cancer to treat in the United States and pharmacoeconomic studies suggest that screening for bladder cancer could not only save lives but also reduce costs per year-life saved. A pilot study is underway and the urology community should be very supportive of studies to validate this opportunity.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".