Insulin-like Growth Factor-I and Risk of High-Grade Cervical Intraepithelial Neoplasia
Bibliographic record
Abstract
Insulin-like growth factors (IGF) and their binding proteins (IGFBP) have been implicated in the risk of several epithelial or glandular tumors, including prostate cancer, breast cancer, and colon cancer. Cervical cancer, which is also of epithelial origin, has been shown to overexpress receptors for IGF-I, and plasma levels of IGF-I have been positively associated with cervical cancer precursors in one epidemiologic study. In this case-control study, we investigated plasma levels of IGF-I and IGFBP-3 in relation to the risk of histologically confirmed high-grade cervical intraepithelial neoplasia (HGCIN) and the risk of human papillomavirus (HPV) infection. Included in this analysis were 329 cases and 621 controls recruited from clinics affiliated with two Montréal-area hospital centers. We observed a reduced risk of HGCIN for increasing levels of IGF-I, with an adjusted odds ratio (OR) of 0.40 (95% confidence interval, 0.19-0.87) for the highest quartile relative to the lowest quartile of IGF-I. No association was observed between IGFBP-3 levels and HGCIN. Among controls, IGF-I was associated with a decreased risk of being positive for HPV-16 or HPV-18, with an adjusted odds ratio of 0.20 (95% confidence interval, 0.05-0.87) for the highest quartile relative to the lowest quartile of IGF-I. There was no association observed between IGFBP-3 levels and HPV infection status. IGF-I-mediated effects seemed to predominate among women <30 years of age. In contrast to the previously reported study, our results suggest that levels of IGF-I in young women may be inversely associated with HGCIN, a precursor to cervical 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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".