Androgen Deprivation for Prostate Cancer: When and How, the Good and the Bad
Bibliographic record
Abstract
Androgen deprivation therapy (ADT) is the mainstay systemic treatment of prostate cancer because of the androgen dependence of the disease. Although ADT has long been used to manage prostate cancer, its use continues to evolve as data from clinical trials mature and long-term effects are recognized. For patients with localized disease and high-risk features, short and long courses of ADT as neoadjuvant/adjuvant therapy have been shown to improve survival when used with radiation therapy, but this has not been demonstrated with radical prostatectomy. The role of ADT with salvage radiotherapy after radical prostatectomy continues to be defined. Lifelong ADT in patients with node-positive disease after surgery or with radiation is also associated with increased survival. Increasingly though, the adverse effects of ADT that go beyond those on libido and hot flashes are being acknowledged. The metabolic effects on lipids, glycemic control, and bone loss from ADT can lead to an increased risk of cardiovascular events and osteoporosis, which needs to be considered when deciding to initiate and treat patients with ADT. Large, randomized trials comparing intermittent to continuous ADT have now been reported. Although the hope for improved cancer outcomes with intermittent therapy has not come to realization, an interrupted approach to therapy may help mitigate some of the negative effects of ADT in selected patients by allowing for off-treatment intervals.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".