The Role of Diabetes in Acromegaly Associated Neoplasia
Bibliographic record
Abstract
BACKGROUND: The risk and mortality due to cancer in patients with acromegaly have been previously investigated. Although GH/IGF-1 excess provides a probable pathophysiological explanation, the degree of IGF-1 excess and the role in acromegaly-associated neoplasms of diabetes, a common comorbidity in acromegaly with known association with cancer, remains unclear. METHODS: Acromegalic patients treated in three Canadian referral centers (Toronto, Montreal, Edmonton) were included. All available clinical information was recorded including: age, initial and last percentage of the upper limit of normal (%ULN) IGF-1 levels, comorbidities and other neoplasms (benign and malignant). RESULTS: 408 cases were assessed. 185 were women (45.3%), 126 (30.9%) developed extra-pituitary neoplasms: 55 malignant and 71 benign. The most frequent anatomic site was the gastrointestinal tract (46 [11.3%]), followed by head and neck (36 [8.8%]) and multiple locations (14 [3.4%]). 106 (26.0%) cases had diabetes. Initial IGF-1 was significantly higher in men older than 50 (380.15 vs. 284.78, p = 0.001) when compared to men younger than 50. Diabetics showed significantly higher initial IGF-1 (389.38 vs. 285.27, p = 0.009), as did diabetics older than 50 compared with those without diabetes. 45.3% (48/106) of cases with diabetes developed extra-pituitary neoplasms vs. 24.3% (71/292) without diabetes (p = 0.001, OR: 2.576 95%CI 1.615-4.108). 22.6% (24/106) of cases with diabetes developed malignant tumors vs. 9.2% (27/292), (p < 0.001, OR 2.873, 95%CI 1.572-5.250). CONCLUSIONS: These data suggest that acromegalic patients with diabetes are more likely to develop extra-pituitary neoplasms and their initial IGF-1 levels are higher. The contribution of IGF-1 vs. diabetes alone or in combination in the development of extra-pituitary neoplasms warrants further investigation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".