Bibliographic record
Abstract
The search for more effective treatments for advanced ovarian cancer continues as results achieved with current strategies leave considerable room for improvement. “Progression-free survival, even in optimally debulked patients, is less than 2 years,” British oncologist Stanley Kaye, M.D., said during an educational symposium at the European Cancer Conference in Copenhagen in September. “We need to do better. The majority of patients will still have died by 5 years.” Ongoing investigations focus in large part on the development of more effective chemotherapy regimens for primary treatment and relapsed disease. Early clinical evaluations of chemotherapy for ovarian cancer suggested three principles that remain inherent to treatment strategies today: immediate platinum-based therapy is superior to non-platinum therapy, a platinum-based combination is superior to single-agent platinum therapy, and cisplatin and carboplatin yield equivalent outcomes. Regimens that include anthracyclines showed some promise as first-line therapy for ovarian cancer until results of the second International Collaborative Ovarian Neoplasm (ICON2) trial failed to demonstrate superiority of CAP (cyclophosphamide–doxorubicin–cisplatin) over carboplatin monotherapy, said Nicoletta Colombo, M.D., a gynecologic oncologist at the European Institute of Oncology in Milan, Italy. The surprising finding that a combination was no better than monotherapy was attributed by some observers to inadequate dosing of cisplatin in the CAP regimen. Despite the findings of ICON2 and other studies, the combination of cyclophosphamide and platinum-based drugs evolved as standard first-line therapy in many parts of the world.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.041 | 0.021 |
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".