Bibliographic record
Abstract
PURPOSE OF REVIEW: The present review focuses on the most significant recent findings regarding selective estrogen receptor modulators (SERMs) and selective androgen receptor modulators (SARMs). SERMs, which interact with estrogen receptor-alpha and estrogen receptor-beta in multiple tissues, continue to generate clinical interest in potential applications in as many disorders as the tissues in which the two known receptors are found. SARMs have been demonstrated to have fewer clinical applications to date, but continue to be investigated for use in multiple disorders in which androgen receptor modulation is likely to be important. Both types of compounds hold great promise for therapeutic use in multiple hormonal disorders involving tissue-specific effects mediated by estrogen or androgen receptors. RECENT FINDINGS: Although SERMs have been available for clinical use for 50 years, recent investigation has focused on large randomized clinical trials for newer indications of older agents or smaller clinical trials of newer agents with improved clinical activity and reduced side effects in specific tissues. In particular, the large, prospective, randomized, controlled, multiyear Study of Tamoxifen and Raloxifene and Raloxifene Use in the Heart clinical trials have recently shown interesting similarities and differences between tamoxifen and raloxifene in estrogen-responsive tissues. Lasofoxifene and arzoxifene are two newer SERMs that have recently been demonstrated to improve bone mineral density and lower serum cholesterol values compared with older SERMs in smaller clinical trials. SARMs are a newer category of drug still being investigated mostly at the basic and preclinical level, with fewer clinical trials available for review. SARMs are currently being investigated mostly for use in prostate cancer at different stages but hold promise for multiple other applications. SUMMARY: Recent clinical trials indicate that SERMs are useful in treatment of disorders of bone and mineral metabolism and breast cancer and in reduction of cardiovascular risk factors. SARMs offer important benefits for management of prostate cancer at different stages, as well as other disorders.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".