An update on chemoprevention strategies in prostate cancer for 2006
Bibliographic record
Abstract
PURPOSE OF REVIEW: An increasing volume of research has been directed at the prevention of prostate cancer. This review proposes to summarize the large trials, novel approaches and molecular mechanisms of effect published in 2004 and 2005. RECENT FINDINGS: The impact of the Prostate Cancer Prevention Trial continues and subsequent articles have addressed the increase of high-grade prostate cancers detected in the finasteride arm of the trial, as well as the potential costs and benefits of extrapolating the findings to a public health campaign. Studies of risk have been published warning of excessive vitamin E and cyclooxygenase-2 inhibitor use in chemoprevention. Growing evidence supports the concept of chemopreventative agent combinations and further data on the roles of selenium, lycopene, soy, green tea, anti-inflammatories and statins in prostate-cancer prevention are presented. SUMMARY: Level one evidence exists for the preventative effects of finasteride in prostate cancer. The evidence for other agents is less conclusive but a number of large-scale, appropriately designed trials will hopefully address some of the relevant issues in prostate-cancer prevention over the next decade.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.012 |
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".