Considerations for Chemotherapy Treatment in Platinum Resistant High-Grade Serous Ovarian Cancer
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.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".