Plasma insulin‐like growth factor 1 is positively associated with low‐grade prostate cancer in the Health Professionals Follow‐up Study 1993–2004
Bibliographic record
Abstract
The insulin-like growth factor (IGF) axis plays a role in growth and progression of prostate cancer. High circulating IGF-1 levels have been associated with an increased risk of prostate cancer. Results for IGF binding protein 3 (IGFBP-3) are inconclusive. Some studies have indicated that the positive association with IGF-1 is observed only for low-grade prostate cancer (Gleason sum < 7). We previously reported in the Health Professionals Follow-up Study (HPFS) a direct positive association between ELISA-measured plasma IGF-1 and IGFBP-3 and risk of prostate cancer (462 cases diagnosed after providing a blood specimen (between 1993 and 1995), but before February 1998). With additional follow-up through January 31st 2004, and 1,331 case-control pairs in total, we were now able to investigate low-grade (Gleason sum < 7, n = 635) and high-grade (Gleason sum ≥ 7, n = 515) prostate cancer separately. Matched odds ratios (OR) and 95% confidence intervals (CI) were estimated using conditional logistic regression. ORs of total prostate cancer comparing top to bottom quartiles were 1.41 (95% CI 1.12-1.78, p-trend = 0.001) for IGF-1 and 1.58 (95% CI 1.24-2.01, p-trend = 0.003) for IGFBP-3. IGF-1 was more strongly associated with low-grade (OR = 1.61 top versus bottom quartile, 95% CI 1.16-2.25, p-trend = 0.01), than with high-grade (OR = 1.29, 95% CI 0.89-1.88, p-trend = 0.12) prostate cancer (p-heterogeneity = 0.08). We hypothesize that these findings reflect that high-grade prostate cancers are more autonomous, and, thus, less sensitive to the action of IGF-1 than low-grade cancers.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".