Serum human glandular kallikrein (hK2) and insulin‐like growth factor 1 (IGF‐1) improve the discrimination between prostate cancer and benign prostatic hyperplasia in combination with total and %free PSA
Bibliographic record
Abstract
BACKGROUND: There is growing evidence describing an association of hK2 and IGFs with cancer. The aim of this study is to investigate the differences in serum levels of hK2 and IGFs in a large group of patients with benign prostatic hyperplasia (BPH) or prostatic carcinoma (CaP) and to examine the value of these variables, as well as their various combinations with PSA, for discriminating between these two clinical entities. METHODS: Human glandular kallikrein 2 (hK2), insulin-like growth factor-1 (IGF-1), free and total PSA concentrations were measured with non-competitive immunological procedures. Receiver operating characteristic (ROC) analysis as well as univariate and multivariate logistic regression analysis were performed to investigate the potential utility of the various markers and their combinations for discriminating between BPH and CaP. RESULTS: hK2 and IGF-1 concentrations were increased in CaP patients, in comparison to BPH patients. hK2/free PSA and free/total PSA ratios (area under the curve, AUC = 0.70) were stronger predictors of prostate cancer than the IGF-1/total PSA ratio (AUC = 0.56) in the group of patients with total PSA <4 microg/L. The hK2/free PSA ratio (AUC = 0.74) was found to have significant discriminatory value in patients with total PSA within the "gray zone" (4-10 microg/L). Multivariate logistic regression models confirmed the observed relationships and identified IGF-1/free PSA and hK2/free PSA as independent predictors of CaP. CONCLUSIONS: hK2/free PSA and IGF-1/free PSA ratios may be useful adjuncts in improving patient selection for prostate biopsy.
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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 | 0.001 | 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".