Critical questions in ovarian cancer research and treatment: Report of an American Association for Cancer Research Special Conference
Bibliographic record
Abstract
Substantial progress has been made in understanding ovarian cancer at the molecular and cellular level. Significant improvement in 5-year survival has been achieved through cytoreductive surgery, combination platinum-based chemotherapy, and more effective treatment of recurrent cancer, and there are now more than 280,000 ovarian cancer survivors in the United States. Despite these advances, long-term survival in late-stage disease has improved little over the last 4 decades. Poor outcomes relate, in part, to late stage at initial diagnosis, intrinsic drug resistance, and the persistence of dormant drug-resistant cancer cells after primary surgery and chemotherapy. Our ability to accelerate progress in the clinic will depend on the ability to answer several critical questions regarding this disease. To assess current answers, an American Association for Cancer Research Special Conference on "Critical Questions in Ovarian Cancer Research and Treatment" was held in Pittsburgh, Pennsylvania, on October 1-3, 2017. Although clinical, translational, and basic investigators conducted much of the discussion, advocates participated in the meeting, and many presentations were directly relevant to patient care, including treatment with poly adenosine diphosphate ribose polymerase (PARP) inhibitors, attempts to improve immunotherapy by overcoming the immune suppressive effects of the microenvironment, and a better understanding of the heterogeneity of the disease.
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 | 0.064 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.025 | 0.028 |
| Insufficient payload (model declined to judge) | 0.007 | 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".