Body fat in nonobese women with prolactinoma treated with dopamine agonists
Bibliographic record
Abstract
OBJECTIVES: To evaluate body fat in nonobese women with prolactinoma treated with dopamine agonists, using whole body dual energy X-ray absorptiometry (DXA) and to correlate DXA results with biochemical data and clinical aspects of the prolactinoma. DESIGN, PATIENTS AND MEASUREMENTS: A cross-sectional study was performed in two University referral centres. Thirty-one nonobese premenopausal women with prolactinoma were subjected to DXA and blood analysis at clinical evaluation. They were compared with 21 control women of similar age and body mass index (BMI). RESULTS: Women with prolactinoma treated with dopamine agonists and controls had similar body fat percentages in all sites evaluated with DXA (arms, legs, trunk, android, gynoid and total body). Patients with normal PRL levels at study entry had lower body fat percentages in all sites. In the patient group, arm, leg, truncal, android, gynoid and total body fat were positively associated with PRL levels. CONCLUSION: Body fat percentage is similar in nonobese women with prolactinoma and in controls. The lower body fat content in patients with normal PRL levels is likely to be due to the metabolic effects of adequate dopamine receptor type 2 (DR2) activation as a result of regular dopamine agonist treatment. This finding reinforces the importance of the appropriate treatment with dopamine agonists in women with prolactinoma, which, besides normalizing PRL levels, reduces body fat content and the consequent risk of developing Metabolic Syndrome and its complications.
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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 | 0.000 | 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) | 0.001 | 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".