Metabolic Glucose Status and Pituitary Pathology Portend Therapeutic Outcomes in Acromegaly
Bibliographic record
Abstract
INTRODUCTION: Acromegaly is frequently associated with impaired glucose tolerance and/or diabetes. To evaluate the relationship between glucose metabolism and acromegaly disease, we evaluated 269 consecutive patients from two referral centres. METHODS: Clinical presentation, pituitary tumor size and invasiveness, and pituitary pathology were captured in a dedicated database. RESULTS: 131 women and 138 men with a mean age of 53.8 years were included. Of these, 201 (74.7%) presented with a macroadenoma and 18 (6.7%) with a microadenoma. Radiographic invasion was present in 91 cases (33.8%). Mean tumor diameter was 1.86 cm (0.2-4.6). Pituitary histopathologic findings revealed pure GH-producing somatotroph adenomas (SA) in 147 patients, prolactin-production by mixed lactotroph (LA) and SA or mammosomatotroph adenoma (MSA) in 46 [22.4%], acidophil stem cell adenoma in 6 [2.9%], and other diagnoses in 6 [2.9%]. Medical treatment included octreotide in 96 [36.9%] and in combination with pegvisomant or dopamine agonists in 63 [24.2%]. Nearly 80% of patients achieved IGF-1 normalization. Importantly, patients with pure somatotroph adenomas were significantly more likely to present with abnormal glucose metabolism [48.7%] than those with mixed adenomas [9.7%] [p<0.001] independent of GH/IGF-1 levels or tumor invasiveness. Abnormal glucose metabolism and pituitary pathology also remained linked following IGF-1 normalization. Moreover patients with pure SA and abnormal glucose metabolism were significantly (p<0.001) less likely to achieve disease remission despite the same therapeutic strategies. Conversely, patients with mixed adenomas were more likely (OR: 2.766 (95% CI: 1.490-5.136) to achieve disease remission. CONCLUSIONS: Patients with pure somatotroph adenomas are more likely than those with mixed adenomas to exhibit abnormal glucose metabolism.
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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 | 0.001 | 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".