A Prospective Study of the Insulin-Like Growth Factor Axis in Relation with Prostate Cancer in the SU.VI.MAX Trial
Bibliographic record
Abstract
Several epidemiologic studies have examined with diverging results the relationships between circulating levels of insulin-like growth factors (IGF) and of IGF-binding proteins (IGFBP) and prostate cancer risk. We assessed the association of prediagnostic plasma levels of IGF-I, IGF-II, IGFBP-2, and IGFBP-3 and subsequent occurrence of prostate cancer in a case-control study nested in the SU.VI.MAX trial. The SU.VI.MAX study was a primary prevention trial testing a daily supplementation with low-dose antioxidant vitamins and minerals in male and female middle-aged volunteers in France. One hundred prostate cancer cases were diagnosed among 4,855 SU.VI.MAX participants over a 9-year follow-up period. For each case, four age-matched controls were selected randomly. Frozen baseline plasma samples were used to measure IGF-I, IGF-II, IGFBP-2, and IGFBP-3. Conditional logistic regression was used to assess the association between these four biochemical markers and prostate cancer risk. After controlling for the intervention group in the trial and the other IGF axis variables, the odds ratios and 95% confidence interval (95% CI) comparing the upper quartile to the baseline quartile were 1.83 (95% CI, 0.85-3.95), 1.05 (95% CI, 0.35-3.18), 0.79 (95% CI, 0.39-1.58), and 0.42 (95% CI, 0.12-1.52) for IGF-I, IGF-II, IGFBP-2, and IGFBP-3, respectively. More suggestive associations for IGF-I and IGFBP-3 were observed with advanced and aggressive cancers. Our results are consistent with those of some previous prospective studies and suggest that IGF axis variables are not long-term predictors of the occurrence of 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".