Malignant peritoneal effusion acting as a tumor environment in ovarian cancer progression: Impact and significance
Bibliographic record
Abstract
Until recently, ovarian cancer research has mainly focused on the tumor cells themselves ignoring for the most part the surrounding tumor environment which includes malignant peritoneal effusions. However, one of the major conceptual advances in oncology over the last few years has been the appreciation that cancer progression cannot be explained by aberrations in cancer cells themselves and is strongly influenced by the surrounding tumor environment. The mechanisms of ovarian cancer progression differ from that of other solid tumors because ovarian cancer cells primarily disseminate within the peritoneal cavity. Malignant peritoneal effusion accumulates in the peritoneal cavity during ovarian cancer progression. These exudative fluids act as a unique tumor environment providing a framework that orchestrates cellular and molecular changes contributing to aggressiveness and disease progression. The composition of ascites, which includes cellular and acellular components, constantly adapts during the course of the disease in response to various cellular cues originating from both tumor and stromal cells. The tumor environment that represents peritoneal effusions closely constitute an ecosystem, with specific cell types and signaling molecules increasing and decreasing during the course of the disease progression creating a single complex network. Although recent advances aiming to understand the ovarian tumor environment have focused one at a time on components, the net impact of the whole environment cannot be understood simply from its parts or outside is environmental context.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".