“BRCAness” Syndrome in Ovarian Cancer: A Case-Control Study Describing the Clinical Features and Outcome of Patients With Epithelial Ovarian Cancer Associated With <i>BRCA1</i> and <i>BRCA2</i> Mutations
Bibliographic record
Abstract
PURPOSE: We evaluated the clinical impact of germ-line BRCA1/2 mutations in patients with epithelial ovarian cancer (EOC) on responses to first and subsequent lines of chemotherapy, treatment-free interval (TFI) between each line of therapy, and overall survival (OS). PATIENTS AND METHODS: Twenty-two EOC patients with germ-line BRCA1 or BRCA2 mutations (BRCA-positive) were selected from our database and matched (1:2) with 44 nonhereditary EOC controls (defined by no associated personal history of breast cancer and no family history of breast and ovarian cancer or an uninformative BRCA mutation test) for stage, histologic subtype, age, and year of diagnosis. All patients received primary platinum-based chemotherapy. Statistical comparisons included responses after first-, second-, and third-line treatment (chi(2)/Fisher's exact test) and median OS (Kaplan-Meier method/log-rank test). RESULTS: Compared with controls, BRCA-positive patients had higher overall (95.5% v 59.1%; P = .002) and complete response rates (81.8% v 43.2%; P = .004) to first line treatment, higher responses to second and third line platinum-based chemotherapy (second line, 91.7% v 40.9% [P = .004]; third line, 100% v 14.3% [P = .005]) and longer TFIs. A significant improvement in median OS in BRCA-positive patients compared with controls was observed from both time of diagnosis (8.4 v 2.9 years; P < .002) and time of first relapse (5 v 1.6 years; P < .001). BRCA status, stage, and length of first response were independent prognostic factors from time of first relapse. CONCLUSION: BRCA-positive EOC patients have better outcomes than nonhereditary EOC patients. There exists a clinical syndrome of BRCAness that includes serous histology, high response rates to first and subsequent lines of platinum-based treatment, longer TFIs between relapses, and improved OS.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
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".