Considerations for Chemotherapy Treatment in Platinum Resistant High-Grade Serous Ovarian Cancer
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
Ovarian cancer is considered to be the most fatal type of any gynecological cancer. Prognosis for the disease is poor, with a median survival of only thirty-two months following diagnosis and a five-year survival rate of only 39%. Many of the most lethal ovarian cancer cases are classified as part of the high-grade serous ovarian cancer (HGSOC) subtype, which is the most aggressive form of the disease. The primary concern with regards to treatment is that nearly 30% of patients will develop a resistance to forms of platinum chemotherapy, which is the main method of treatment. This suggests that a one-size fits all approach cannot be taken to treat ovarian cancer, and that further research must be done to understand how to treat the patients who present with platinum resistance. This literature review examines the mutations within two susceptible loci, specifically, the p53 and BRCA1/2 genes, in order to understand how platinum resistance develops and why it is present in some patients. The objectives of this review are to characterize the underlying genetic mechanisms affecting platinum resistance, specify the biomarkers associated with those mechanisms, and describe alternative methods for approaching the treatment of ovarian cancer on an individual scale.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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