Exploring the Clinical Impact of Predictive Biomarkers in Serous Ovarian Carcinomas
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
Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy. Although initial response rates to standard platinum-based treatment are at 70-80%, long-term response in advanced EOC disease is rarely achieved with the development of chemoresistance and recurrence, contributing to overall survival rates below 45%. Additional challenges stem from EOC heterogeneity, reflecting at least five histological subtypes, each with different underlying molecular characteristics and clinicopathology that have significant implications in treatment effectiveness and management. Since the last decade, technologies in genomics, proteomics and pathology have been deployed to find reliable clinical markers that can identify patients sensitive to standard chemotherapy treatments and stratify patients for more suitable targeted therapies. These efforts have identified several molecular markers of prognostic value that have been validated as biomarkers, such as BRCA and KRAS mutations, or are currently under investigation in clinical trials, such as CD8 T cells, immune checkpoint inhibitors and progesterone receptor. Recent advancements in biomarker research have also revealed new targets that have expanded treatment options, introducing poly (ADP-ribose) polymerase (PARP) inhibitors, anti-angiogenic agents, inhibitors targeting signaling pathways, and immunotherapy to improve maintenance therapies or enhance first-line therapy. This review presents a summary of current biomarkers, in clinical use or under evaluation, demonstrating a potential to inform on patient selection for treatment efficacy and predict response to EOC therapies, with particular focus on the serous subtypes, including high-grade and low-grade serous carcinomas.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| 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) | 0.000 | 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