Insulin-like growth factor-1 and insulin-like growth factor binding protein-3 for prostate cancer detection in patients undergoing prostate biopsy.
Bibliographic record
Abstract
PURPOSE: Laboratory and epidemiological studies suggest that high circulating insulin-like growth factor (IGF)-1 and low IGF binding protein-3 are associated with increased prostate cancer risk. However, the usefulness of serum IGF-1 or IGF binding protein-3 for predicting pathology results in men undergoing prostate biopsy is unclear. We examined the relationships of serum IGF-1, IGF binding protein-3 and the results of prostate biopsy. MATERIALS AND METHODS: A total of 652 consecutive patients with elevated serum prostate specific antigen (PSA) or abnormal digital rectal examination who were referred for transrectal ultrasound sextant prostate needle biopsy underwent blood sampling before biopsy. PSA, free PSA, IGF-1 and IGF binding protein-3 were measured. There were 244 men (37.4%) with cancer and 408 controls with benign conditions. RESULTS: Mean IGF-1 plus or minus SD in the cancer and control groups was 176.1 +/- 58.3 and 178.7 +/- 54.7 ng./ml., respectively (p = 0.57). Mean IGF binding protein-3 in the cancer and control groups was 2,724 +/- 647 and 2,673 +/- 589 ng./ml., respectively (p = 0.3). Adjustment for age and PSA showed significantly lower IGF-1 in cancer cases, while IGF binding protein-3 was not significant. ROC values were significantly higher for free-to-total PSA and PSA than for crude and age adjusted IGF-1 and IGF binding protein-3. CONCLUSIONS: Our data indicate that serum IGF-1 or IGF binding protein-3 does not predict the results of prostate biopsy in men with elevated PSA or abnormal digital rectal examination. This finding implies that while there is evidence that the IGF-1 level is a risk factor for prostate cancer, neither IGF-1 nor IGF binding protein-3 can be used as a tumor marker for this disease.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".