Association between Components of the Insulin-Like Growth Factor System and Endometrial Cancer Risk
Bibliographic record
Abstract
OBJECTIVE: The insulin-like growth factor (IGF) system has been related to cell proliferation, obesity, diabetes, hyperinsulinemia and endometrial cancer risk. We used data from a case-control study conducted in Italy to provide additional information on the relation between the IGF system and endometrial cancer. METHODS: A case-control study was conducted between 1999 and 2002 in Italy, including a total of 73 women with incident, histologically confirmed endometrial cancer and 108 controls admitted to the same hospital network for acute, nonneoplastic diseases. All subjects were interviewed using a validated questionnaire. RESULTS: The odds ratios for endometrial cancer comparing the highest versus the lowest tertile of various IGF components were as follows: 0.5 [95% confidence interval (CI) 0.2-1.2] for free IGF-I, 1.1 (95% CI 0.5-2.6) for total IGF-I, 1.2 (95% CI 0.6-2.6) for total IGF-II, 2.4 (95% CI 1.0-5.9) for IGF binding protein (IGFBP)-1 and 0.8 (95% CI 0.4-2.0) for IGFBP-3. Further allowance for all IGF components in the model did not modify the results. The direct relation with IGFBP-1 was stronger and limited to heavier and older women. CONCLUSIONS: The present findings suggest a limited effect of the IGF system on endometrial cancer risk. Increasing IGFBP-1 levels seem to be associated with endometrial cancer risk in older women and in women with a higher body mass index.
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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) | 0.002 | 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".