Prognostic significance of IL-6 and IL-8 ascites levels in ovarian cancer patients
Bibliographic record
Abstract
BACKGROUND: The acellular fraction of epithelial ovarian cancer (EOC) ascites promotes de novo resistance of tumor cells and thus supports the idea that tumor cells may survive in the surrounding protective microenvironment contributing to disease recurrence. Levels of the pro-inflammatory cytokines IL-6 and IL-8 are elevated in EOC ascites suggesting that they could play a role in tumor progression. METHODS: We measured IL-6 and IL-8 levels in the ascites of 39 patients with newly diagnosed EOC. Commercially available enzyme-linked immunosorbent assay (ELISA) was used to determine IL-6 and IL-8 ascites levels. Ascites cytokine levels were correlated with clinicopathological parameters and progression-free survival. RESULTS: Mean ascites levels for IL-6 and IL-8 were 6419 pg/ml (SEM: 1409 pg/ml) and 1408 pg/ml (SEM: 437 pg/ml) respectively. The levels of IL-6 and IL-8 in ascites were significantly lower in patients that have received prior chemotherapy before the surgery (Mann-Whitney U test, P = 0.037 for IL-6 and P = 0.008 for IL-8). Univariate analysis revealed that high IL-6 ascites levels (P = 0.021), serum CA125 levels (P = 0.04) and stage IV (P = 0.009) were significantly correlated with shorter progression-free survival. Including these variables in a multivariate analysis revealed that elevated IL-6 levels (P = 0.033) was an independent predictor of shorter progression-free survival. CONCLUSION: Elevated IL-6, but not IL-8, ascites level is an independent predictor of shorter progression-free survival.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".