Abstract 2795: Serum IGF-I, IGF-II, IGFBP-3, and IGF-I/IGFBP-3 molar ratio and risk of pancreatic cancer in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial
Bibliographic record
Abstract
Abstract Experimental evidence suggests that an overexpression of insulin-like growth factor-I (IGF-I) is implicated in human pancreatic tumors. Moreover, increased IGF-II and decreased insulin-like growth factor binding protein-3 (IGFBP-3) concentrations are found in a number of cancers. We conducted a nested case-control study in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial cohort of men and women 55-74 years of age at baseline, to test whether pre-diagnostic circulating IGF-I, IGF-II, IGFBP-3, and IGF-I/IGFBP-3 molar ratio concentrations were associated with exocrine pancreatic cancer risk. Between 1994 and 2006, 187 incident cases of pancreatic adenocarcinoma occurred (follow-up to 11.7 years). Two controls (n=374),who were alive at the time the case was diagnosed, were selected for each case and matched by age, race, sex and date of blood draw. We calculated odds ratios (ORs) and 95% confidence intervals (CIs) using conditional logistic regression, adjusting for smoking. In the multivariable adjusted model, IGF-I, IGF-II, and IGFBP-3 concentrations were not associated with pancreatic cancer (highest compared with lowest quintile, OR=1.23, 95% CI 0.67-2.25, p-trend=0.12; OR=0.98, 95% CI 0.52-1.85, p-trend=0.52; and OR=1.15, 95% CI 0.62-2.16, p-trend=0.90). However, a significant positive trend was observed with high IGF-I/IGFBP-3 molar ratio levels in the smoking-adjusted model (OR=1.46, 95% CI 0.78-2.74, p-trend=0.04). A higher IGF-I/IGFBP-3 molar ratio may represent increased free IGF-I and be a risk factor for pancreatic cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2795.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".