Syndecan‐1 expression in prostate cancer and its value as biomarker for disease progression
Bibliographic record
Abstract
OBJECTIVE: To evaluate the association between syndecan-1 (CD138) expression and prostate cancer. PATIENTS AND METHODS: We evaluated syndecan-1 expression using a recently constructed tissue microarray of prostatic samples taken from 243 patients, corresponding to 1400 cores, with 69.8%, 5.6%, 17.6% and 7% of the cores representing localized prostate cancer, high-grade prostatic intraepithelial neoplasia, benign prostate tissue and hormone refractory/metastatic disease, respectively. RESULTS: Metastatic cases had the highest frequency and membranous staining intensity for syndecan-1 overexpression, followed by hormone refractory and localized disease (83.3% vs 34.8% and 25.7%, respectively). There was no significant difference in the frequency of membranous syndecan-1 expression between localized prostate cancer and benign glands (25.7% vs 24.7% of cases, respectively). However, benign glands showed significantly higher intensity staining than localized prostate cancer. We found no significant association between syndecan-1 expression and any of the following: Gleason score, pathological stage, surgical margin status and biochemical recurrence. CONCLUSION: The current available evidence, from the present and previous studies, show that syndecan-1 is not an independent predictor of recurrence or tumour-specific survival, diminishing its significance as a clinical marker.
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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 | 0.000 |
| 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".