Manage wisely: poly (ADP-ribose) polymerase inhibitor (PARPi) treatment and adverse events
Bibliographic record
Abstract
Poly (ADP-ribose) polymerase (PARP) inhibitors (PARPi) have transformed the treatment landscape in front-line and recurrent high-grade serous ovarian cancer. Maintenance strategies with PARPi have been assessed in randomized phase III trials in ovarian cancer; switch maintenance in the case of olaparib, niraparib, and rucaparib; and concurrent followed by continuation maintenance with veliparib. These studies have shown progression-free survival advantage with PARPi maintenance, with no major adverse changes in the quality of life; however, overall survival data remain immature to date. PARPi have also been incorporated in clinical practice as a single-agent treatment strategy in high-grade serous ovarian cancer, mainly in women who harbor alterations in the BRCA1/2 genes or have alterations in the homologous recombination deficiency (HRD) pathway. Contemporary studies are looking into potentially synergistic combination strategies with anti-angiogenics and immune checkpoint inhibitors, among others. The expansion of PARPi treatment has not been limited to ovarian cancer; talazoparib is licensed in patients with HER2-negative breast cancer with germline BRCA mutations (BRCAm), and front-line olaparib maintenance in patients with pancreatic cancer with germline BRCAm. Numerous studies assessing PARPi either in monotherapy or in combination with other agents are ongoing in multiple tumors, including prostate, endometrial, brain, and gastric cancers. Many patients are being treated with PARPi, some for prolonged periods of time. As a result, a thorough knowledge of the potential short- and long-term adverse events and their management is warranted to improve patient safety, treatment efficacy, and towards maintaining an appropriate dose intensity.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".