The impact of T‐cell immunity on ovarian cancer outcomes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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 it