<p>Serum Adipokines as Predictors for the Outcome of Prostate Biopsies at Early Stage Prostate Cancer Diagnosis</p>
Bibliographic record
Abstract
PURPOSE: Elevated adipokines in patients with obesity and metabolic syndrome have been linked to increased risk of prostate cancer (PCa). The association between select serum adipokines and the outcome of prostate biopsies alone and in combination with clinical parameters at different early stages of PCa was investigated. PATIENTS AND METHODS: Clinical data and serum adipokines were retrieved from three retrospective cohorts representing men at different points in PCa detection: 1. Subjects with no prior biopsies (n=1061), 2. subjects with a prior negative biopsy (REDUCE trial, control arm) (n=1209), 3. subjects with low-risk PCa on active surveillance (AS) (n=154). Adipokines were chosen based on an unpublished pilot study and included: Resistin, Tumor Necrosis Factor-α, Interleukin-6, Monocyte Chemoattractant Protein-1, Hepatocyte Growth Factor, and Nerve Growth Factor. The primary outcome was the absence of PCa on biopsy and the secondary outcome was diagnosis of low-risk PCa fitting the criteria for continuing AS. Logistic regression analysis was used to assess the association of adipokines and negative and/or low-risk PCa at prostate biopsy. RESULTS: In men with no prior prostate biopsy or with prior negative biopsy, adipokines were not predictors of prostate biopsy outcomes on multivariable regression analysis controlling for known clinical variables. In the AS cohort, MCP-1 and Resistin were significant predictors of biopsy outcome on multivariable analysis (OR 0.20, 95% CI: 0.05-0.85, p= 0.03 & OR 0.30, 95% CI: 0.10 -0.86, p= 0.03). CONCLUSION: Our findings do not support a strong role for adipokines for predicting the outcome of prostate biopsies at any early stage in PCa diagnosis.
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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 | 0.001 | 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".