Evaluation of sICAM-1, sVCAM-1, and sE-Selectin Levels in Patients with Metastatic Breast Cancer Receiving High-Dose Chemotherapy
Bibliographic record
Abstract
Soluble forms of some cell adhesion molecules (CAM), sICAM-1, sVCAM-1, and sE-selectin, are elevated in the sera and plasma of patients with inflammation, arthritis, diabetes, and cancer. Increased levels of these soluble molecules in patients with cancer have been shown to correlate with disease progression and survival. This suggests that increased expression of the soluble forms of CAMs may play an important role in cancer cell growth and metastasis and may be prognostic and/or predictive of malignant disease. In this retrospective study, we assessed the clinical significance of sICAM-1, sVCAM-1, and sE-selectin in 95 patients with metastatic breast cancer enrolled in clinical trials of high-dose chemotherapy (HDC) and autologous stem cell transplantation (ASCT). The significance of soluble HER-2 (sHER-2) and sFAS status, determined in previous studies for this group of patients, was also included in this analysis. Univariate analysis showed that sICAM-1, sVCAM-1, sFas, sHER-2 positive status, and the presence of liver metastases were significant prognostic factors for both progression-free survival (PFS) and overall survival (OS) in the total patient group. In multivariable analysis, HER-2 and sFAS were shown to be independent prognostic factors for PFS and OS. Within the various treatment groups examined, sICAM-1 was a prognostic factor for clinical outcome for patients with metastatic breast cancer enrolled in trials with cyclophosphamide- and carboplatin-based or vinblastine-based HDC, but not in trials with paclitaxeland cyclophosphamide-based HDC.
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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 | 0.000 | 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".