Screening and hormonal therapy of localized prostate cancer shows major benefits on survival.
Bibliographic record
Abstract
Five recent randomized studies demonstrate that treatment of localized prostate cancer saves lives. Since prostate cancer grows insidiously without signs or symptoms until it reaches the bones-when cure has become an exception-screening is essential to diagnose the disease at an earlier stage when cure is possible in the majority of patients. At 7 years' median follow-up with continuous hormonal therapy, cancer-specific death was observed in less than 5% of patients with treatment started at the stage of clinically localized disease, compared to 70% to 90% when treatment is started in patients with bone metastases. With appropriate screening, 99% of prostate cancers can be diagnosed at the localized stage with no sign of bone metastases. Starting androgen blockade at time of diagnosis of localized disease can achieve long-term control of the disease in close to 100% of patients and even cure the disease in the majority of them, as evidenced by maintenance of undetectable PSA in 85% of patients after cessation of long-term combined hormonal therapy. The available data indicate that the use of regular PSA screening and immediate treatment can markedly reduce mortality rates among prostate cancer patients. Next steps toward improving the quality of life for prostate cancer patients including more tolerable treatments and prevention steps are underway.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".