OV21/PETROC: A Randomized Gynecologic Cancer Intergroup Phase II Study of Intraperitoneal Versus Intravenous Chemotherapy Following Neoadjuvant Chemotherapy and Optimal Debulking Surgery in Epithelial Ovarian Cancer
Bibliographic record
Abstract
(Abstracted from Ann Oncol 2018;29(2):431–438) The use of neoadjuvant chemotherapy (NACT) prior to surgical debulking is increasingly used for advanced epithelial ovarian cancer (EOC). In women with EOC, the principal site of disease is the peritoneal cavity. As a means of increasing the dose intensity delivered to the tumor, intraperitoneal (IP) chemotherapy has been investigated. Three randomized clinical trials and a meta-analysis have demonstrated improved survival for women with stage III EOC who—following optimal, primary debulking surgery—received a combination of intravenous (IV) and IP chemotherapy. Updated data from Gynecologic Oncology Group trial 172 (GOG 172), the most recent of these trials, reported continued benefit for women who had received the experimental arm. However, the use of IP/IV chemotherapy for advanced-stage EOC remains controversial. There has been a continuing debate on the impact of drug scheduling on the benefits of IP and concern over the toxicity of IP cisplatin used in the positive studies, compared with IV carboplatin.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".