Efficacy and safety of aromatase inhibitors in early breast cancer
Bibliographic record
Abstract
BACKGROUND: Third-generation aromatase inhibitors (AIs) are surfacing as the standard adjuvant treatment for postmenopausal women with hormone receptor positive breast cancer over tamoxifen but their long-term effects are still under investigation. OBJECTIVE: In the light of current information, what factors should health practitioners take into consideration when prescribing AIs to patients? METHODS: Results of several randomized controlled adjuvant clinical trials were reviewed to assess the efficacy of treatment and their subprotocols focusing on quality of life and skeletal health to highlight the safety concerns. CONCLUSION: To prevent early recurrences, AIs should be considered as the upfront hormonal treatment of choice. They are also recommended for use as a switching strategy after 2-3 years of tamoxifen and as extended adjuvant treatment after 5 years of tamoxifen. The adverse events experienced are manageable and overall quality of life is not compromised; however, bone density must be monitored for patients at risk and appropriate bone-protection supplements need to be taken.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".