Endometrioid Ovarian Carcinoma Benefits from Aromatase Inhibitors: Case Report and Literature Review
Bibliographic record
Abstract
UNLABELLED: Aromatase inhibitors have not been adequately assessed in treatment of ovarian cancer. The aromatase inhibitor letrozole (2.5 mg daily) was administered in 2 cases of advanced endometrioid ovarian cancer with positive estrogen receptor. CASE 1: A 52-year-old woman with a grade 2-3, stage iiic endometrioid ovarian cancer was optimally debulked and received 6 cycles of intravenous paclitaxel and intraperitoneal cisplatin-paclitaxel. Post chemotherapy, one of several biopsies showed residual disease during the second-look laparoscopy. This patient was treated with letrozole and remained disease-free during 30 months of follow-up. CASE 2: A 47-year-old woman with a grade 3, stage iiic endometrioid ovarian cancer was optimally debulked and treated with intravenous carboplatin-paclitaxel. After a 15-month remission, her first recurrent disease was treated with carboplatin-docetaxel. The second remission lasted only 11 months, after which the patient was treated with splenectomy and subsequent liposomal doxorubicin. Letrozole was administered after the chemotherapy. The patient had a 30-month remission before the next recurrence of her disease. CONCLUSIONS: Endometrioid ovarian carcinoma may benefit from aromatase inhibitors, especially when the tumour burden is low after primary chemotherapy or when the inhibitor is used as maintenance therapy between chemotherapies.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".