Cytokines and cell adhesion molecules exhibit distinct profiles in health, ovarian cancer, and breast cancer
Bibliographic record
Abstract
OBJECTIVE: We examined a panel of cytokines and cell adhesion molecules in an attempt to identify cancer specific profiles. DESIGN AND METHODS: Cytokines and cell adhesion arrays (Randox Ltd.) were measured in samples from women with a histological diagnosis of ovarian cancer ([Formula: see text]) or breast cancer ([Formula: see text]) or cancer free ([Formula: see text]). Random forest analysis was used for classification. RESULTS: Ovarian cancer subjects were classified with a sensitivity of 85.7% (95% CI 50-100) and a specificity of 84.2% (95% CI 69.4-93.4). Breast cancer subjects were classified with a sensitivity of 70.8% (95% CI 47.1-86.4) and a specificity of 96.4% (95% CI 82.1-100). DISCUSSION: Cytokine and cell adhesion molecule profiles provide additional information that may be useful for cancer characterization of female cancers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".