Bibliographic record
Abstract
PURPOSE: The purpose of this article is to present an overview of the epidemiology diagnosis, and management of bladder cancer, with a focus on the early stage of this disease. OVERVIEW: English-language articles published between 1990 and 2000, as well as selected abstracts published in non-English languages before 1990, were reviewed. Epidemiologic data clearly indicate that bladder cancer is much more common in men, White persons, and the elderly. Cigarette smoking appears to be the most significant environmental risk factor. Screening for the disease is currently not standard in the United States or Canada. Potential tests include urine cytology, hematuria dipstick, and the urinary biomarkers. Diagnosis is made most often on the basis of the findings of cystoscopy, tumor biopsy, and urine cytology. Transurethral resection (TUR) of the tumor is generally the first-line treatment for superficial disease. Cystectomy is the "gold standard" treatment for invasive disease in many countries, although trimodality therapy (TUR, radiation, systemic chemotherapy) has shown promise as a bladder-preserving strategy. Intravesical therapy is effective for preventing disease recurrence, although its role in slowing disease progression is uncertain. Chemotherapy and radiation also can be used with cystectomy to treat or prevent pelvic recurrence of invasive disease or to prolong life in patients with metastatic disease. CLINICAL IMPLICATIONS: Bladder cancer is a commonly occurring disease. Prevention efforts must focus on the avoidance or cessation of cigarette smoking and on public education relating to known environmental risk factors. Patient and disease factors must be considered in making treatment decisions and determining prognosis. Careful follow-up after treatment is essential. It is hoped that ongoing research on potential tumor markers and tumor-specific therapies ultimately will result in improved clinical outcomes for patients with this malignancy.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.069 | 0.046 |
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".