Manage wisely: poly (ADP-ribose) polymerase inhibitor (PARPi) treatment and adverse events
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 <i>BRCA1/2</i> 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 <i>BRCA</i> mutations (<i>BRCA</i>m), and front-line olaparib maintenance in patients with pancreatic cancer with germline <i>BRCA</i>m. 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 it