Insulin‐like growth factor‐I, insulin‐like growth factor binding protein‐3 and risk of benign prostate hyperplasia in the prostate cancer prevention trial
Bibliographic record
Abstract
BACKGROUND: We investigated whether peptides involved in cellular proliferation and apoptosis, [insulin-like growth factor I (IGFI) and its major binding protein (insulin-like growth factor binding protein 3)], predicted risk of benign prostate hyperplasia (BPH). METHODS: We conducted a nested-case-control study in the placebo arm of the prostate cancer prevention trial (PCPT). Cases (n = 727) were men with surgical or medical treatment for BPH; two or more IPSS scores >14; or two scores of at least five points over baseline one of which was >or=12. Controls (n = 727) were frequency matched by age to cases, reported no BPH treatment, and no IPSS score >8. Cases and controls remained on the PCPT placebo and were followed closely until their 7-year PCPT anniversary. Baseline serum was analyzed for IGFI and IGFBP3. Unconditional logistic regression and polytomous regression estimated the multivariate-adjusted odds ratio (OR) for BPH risk. RESULTS: IGFBP3 was inversely and the IGFI:IGFBP3 ratio was positively associated with BPH risk, but findings were statistically significant only for men with severe symptoms (OR = 0.60, 95% CI = 0.40-0.90 for the fifth vs. first quintile of IGFBP3, P-trend = 0.01). Associations did not differ by age (<65 or >or=65 years), and there was a suggestion that the IGFI:IGFBP3 - BPH risk association may be stronger among overweight men. CONCLUSIONS: A high IGFI:IGFBP3 ratio was associated with increased BPH risk, and high serum IGFBP3 was associated with decreased BPH risk among men with severe symptoms. These results confirm findings from other recent studies.
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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 | 0.004 |
| 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) | 0.001 | 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".