Insulin-Like Growth Factor (IGF)-1, IGF-Binding Protein-3, and Pancreatic Cancer in Male Smokers
Bibliographic record
Abstract
To investigate whether insulin-like growth factor (IGF)-1 and IGF-binding protein-3 (IGFBP-3) are prospectively associated with exocrine pancreatic cancer, we conducted a nested case-control study within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study cohort of 29,133 male Finnish smokers, aged 50-69 years. To avoid the potential influence of subclinical cancer on IGF-1 and IGFBP-3, all subjects in this study were alive without clinical evidence of cancer during their 5th year of the cohort follow-up. Four hundred randomly selected cohort controls and 93 incident pancreatic adenocarcinoma cases that occurred between their 5th follow-up year through 1997 (i.e., up to 12.7 years of follow-up) were included in this study. Concentrations of IGF-1 and IGFBP-3 were measured in serum samples obtained at baseline using ELISA. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using logistic regression models, adjusted for confounders. Neither IGF-1, IGFBP-3, nor the IGF-1:IGFBP-3 molar ratio was significantly associated with pancreatic cancer: highest compared to lowest tertile, OR = 0.67, 95% CI 0.37-1.21, P trend = 0.17; OR = 0.70, 95% CI 0.38-1.27, P trend = 0.12; and OR = 0.85, 95% CI 0.50-1.46, P trend = 0.54, respectively. Our results do not support the hypothesis that serum IGF-1 and IGFBP-3 concentrations are associated with pancreatic cancer risk among male smokers. Further studies are necessary to evaluate these associations in other populations.
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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 | 0.001 |
| 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".