Serum insulin‐like growth factor (IGF)‐1 and IGF‐binding protein‐3 do not correlate with Gleason score or quantity of prostate cancer in biopsy samples
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship of serum insulin-like growth factor (IGF)-1 and IGF binding protein-3 (IGFBP-3) with histological cancer characteristics in men undergoing transrectal ultrasonography (TRUS)-guided biopsy. PATIENTS AND METHODS: Patients (652), with either an elevated serum prostate-specific antigen level or an abnormal digital rectal examination, were initially evaluated by TRUS and sextant prostatic needle biopsy. Blood was drawn before biopsy, serum extracted and stored frozen until IGF-1 and IGFBP-3 were measured. In all, 241 patients had prostate cancer (37%) and were included in this study. The number of positive biopsies, the volume of tumour in each positive biopsy and the Gleason score were recorded. RESULTS: Of the 241 patients, 37 had five or six positive biopsies (from six), 128 had two to four and 76 had one. Serum IGF-1 did not correlate with the number of positive biopsies, with means of 176.7, 178.3 and 164.4 ng/mL, respectively (P = 0.3), while the mean IGFBP-3 was 2695, 2795 and 2572 ng/mL, respectively (P = 0.09). The additive percentiles of tumour volume in positive biopsies were assessed for each patient but serum IGF-1 and IGFBP-3 did not correlate (P = 0.7 and 0.9, respectively). In all, 92 patients had a Gleason score of < 7, 80 a score of 7 and 69 a score of > 7; the mean (sd) IGF-1 levels for the three groups were 181 (39), 174.6 (35) and 176 (26) ng/mL, and the mean IGFBP-3 2798 (240), 2735 (284) and 2647 (221) ng/mL, respectively, none of the differences being statistically significant. CONCLUSIONS: Serum IGF-1 and IGFBP-3 do not correlate with quantity of cancer or Gleason score in biopsy samples from patients with prostate cancer.
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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 | 0.003 |
| 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) | 0.002 | 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".