Trends in Hormonal Management of Prostate Cancer:
Bibliographic record
Abstract
OBJECTIVE: To provide a population-based description of current practice in the use of hormonal management of prostate cancer. DESIGN,SETTING & PARTICIPANTS: All men in Ontario, Canada, age 65 and older, with confirmed prostate cancer starting maintained hormonal therapy, from July 1992 through December 1998 (11,435 patients). Data sources included the provincial drug benefit plan, hospital services data, and Ontario Cancer Registry. OUTCOME MEASURES: Rates and trends in the use of: surgical or medical castration; total androgen blockade (TAB); and monotherapies based on steroidal or nonsteroidal antiandrogens. RESULTS: In 5.5 years, use of 'standard' therapy based on surgical or medical castration alone dropped from 36% to 26% of patients, while the use of TAB doubled from 22% to 41%. Approximately 15% of patients received nonsteroidal antiandrogens without evidence of therapy aimed at central androgen blockade. Marked regional differences were observed and not explained by patient age or practitioner specialty. CONCLUSIONS: New hormonal therapies for prostate cancer have implications in terms of disease control, patient survival, side effects, and costs. Rapid growth in prescribing of antiandrogens may represent an unnecessary expense for public or private payers, and observed regional differences likely reflect lack of consensus on the relative merit of TAB. Patients and practitioners must have current information on the advantages and disadvantages of different therapeutic options, and quality-of life, particularly with respect to emerging drug therapies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".