Effect of weight loss diets on biochemical parameters and anthropometric measurements in prolactinoma patients
Bibliographic record
Abstract
Background: The aims of this study were to determine the effect of weight loss on biochemical parameters and anthropometric measurements in prolactinoma patients and to evaluate the effectiveness of weight loss diet along with medical treatment. Methods: Twenty-two patients with prolactinoma were divided into two groups and one of the groups was applied weight loss diet (diet group) while the other group was diet free (control group). Each participant was interviewed using a structured questionnaire. The biochemical parameters (fasting plasma glucose, fasting plasma insulin, prolactin, leptin, TSH, T4, cortisol, HbA1c, AST, ALT and blood lipids) of participants were analyzed and anthropometric measurements were taken. Results: There was a significant change in mean BMI after treatment in diet group (p=0.000). The mean level of serum prolactin decreased from 45.1±31.63 ng/dL at baseline to 12.6±8.19 ng/dL after three months in diet group (p=0.006). Despite there being no statistically significant difference between diet and control group in terms of baseline level of prolactin measurement (p=0.800), statistically significant difference between the two groups in terms of final level of prolactin measurement (p=0.027) was observed. There was a significant change in mean level of leptin after treatment in diet group (p=0.001). Conclusions: In addition to medical treatment, weight loss diets sped up the healing process for hyperprolactinemia and the reduction in body weight had positive effects on the metabolic profiles of prolactinoma patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".