Current Trends in the Treatment of Human Epidermal Growth Factor Receptor-2 Negative Breast Cancer
Bibliographic record
Abstract
Breast cancer affect almost 1 in 8 women and it is highest in developed and developing countries. There are many drugs exist for the treatment of breast cancer, but still the incidence of mortality and morbidity are high among all cancer types in most countries. Even though the conventional therapies play a major role in the management of breast cancer, its complications are obvious and unavoidable. The newer targeted drug therapy came in place to reduce complications in some extent, but not fully. Hence, a review was aimed to analyse the efficacy and safety of newer anticancer drugs that approved for the treatment of breast cancer by US-FDA from 2017 to 2019. The Olaparib, Talazoparib, and Ribociclib are the newly approved drugs for the treatment of breast cancer during this review period. Among these new drugs, Olaparib and Talazoparib alone or in combination with other anticancer drugs considered as safe and efficacious. Patients with Olaparib or Talazoparib as monotherapy have median progression-free survival of 2.8 to 8.6 months longer and has 42-46% lower risk of death (P
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".