Bibliographic record
Abstract
Ovarian cancer remains a challenging disease for which improved treatments are urgently needed. Most patients present with advanced disease that is highly responsive to surgery combined with platinum- and taxane-based chemotherapy, with a state of minimal residual disease being achieved in many cases. However, chemotherapy-resistant recurrent tumors typically appear within 1-5 years and are ultimately fatal. Recently, several groups have shown that ovarian tumors are often infiltrated by activated T cells at the time of diagnosis, and patients with dense infiltrates of CD3+CD8+ T cells experience unexpectedly favorable progression-free and overall survival. Other cell types in the immune infiltrate oppose anti-tumor immunity, including CD4+CD25+FoxP3+ regulatory T cells, CD8+ regulatory T cells, macrophages, and dendritic cells. The composition of immune infiltrates is shaped by the expression of cytokines, chemokines, antigens, major histocompatibility complex molecules, and costimulatory molecules. The relationship between these various immunological factors is reviewed here with a strong emphasis on outcomes data so as to create a knowledge base that is well grounded in clinical reality. With improved understanding of the functional properties of natural CD8+ T-cell responses to ovarian cancer, there is great potential to improve clinical outcomes by amplifying host immunity.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".