Three new serum markers for prostate cancer detection within a percent free PSA‐based artificial neural network
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: We aimed to evaluate the value of macrophage inhibitory cytokine 1 (MIC-1), human kallikrein 11 (hK11) migration inhibitor factor (MIF) in comparison to prostate-specific antigen (PSA) and % fPSA and also to develop a % fPSA-based ANN with the new input factors to determine whether these additional markers can further eliminate unnecessary prostate biopsies. METHODS: Serum samples from 371 patients with prostate cancer (PCa, n=135) or benign prostate hyperplasia (BPH, n=236) within the PSA range 0.5-20 microg/L were analyzed for total PSA, free PSA, MIC-1, hK11, and MIF. 'Leave one out' ANN models with these variables and prostate volume were constructed and compared to logistic regression (LR) and all single parameters. RESULTS: The discriminatory power of MIC-1, hK11, and MIF was less than that for PSA despite significant differences in BPH compared to PCa patients. At 90% and 95% sensitivity, the artificial neural networks (ANNs) were only significantly better than % fPSA if prostate volume was included. CONCLUSIONS: ANNs with the novel input factors of MIC-1, MIF, and/or hK11 and additional use of prostate volume demonstrated significant advantage compared with % fPSA and tPSA and may lead to a reduction in unnecessary prostate biopsies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it