Are prolactin levels efficient in predicting a pituitary lesion in patients with hyperprolactinemia?
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Bibliographic record
Abstract
PURPOSE: Data regarding the presence of a prolactin (PRL) threshold above which a pituitary magnetic resonance imaging (MRI) is mandatory in patients with hyperprolactinemia (hyperPRL) are controversial and derived primarily from studies focused on female populations. Aim of our study was to evaluate in a cohort of patients of both sexes with confirmed hyperPRL, the possible correlation between PRL values and the presence of pituitary abnormalities. METHODS: We retrospectively analyzed data from patients who underwent serial PRL sampling at our Division between January 2015 and December 2022. Patients diagnosed with monomeric hyperPRL at serial sampling and with subsequent contrast-enhanced MRI results available for the pituitary region were included in the study. Exclusion criteria were prior pituitary disease, severe renal insufficiency, liver cirrhosis, uncompensated primary hypothyroidism and ongoing therapy with hyperprolactinemic drugs. Physiological causes of hyperPRL were also ruled out. RESULTS: Out of the 1253 patients who underwent serial PRL sampling, 139 patients (101 women and 38 men) met the inclusion criteria: 106 (76.3%) patients had some form of pituitary disease, with microlesions observed in 69.8%, macrolesions in 25.5% and other findings in 4.7% of subjects. PRL values showed a modest accuracy in predicting the presence of a pituitary abnormality and the best cut-offs identified were >25 µg/L (AUC 0.767, p = 0.003) and >44.2 µg/L (AUC 0.697, p < 0.001) in men and women, respectively; however, if only patients with PRL values > 500 µg/L were excluded from the analysis, as they were already supposed to harbor a macroprolactinoma, PRL levels were not able to predict the presence of a macrolesion neither in men nor women. CONCLUSION: Given the high prevalence of pituitary abnormalities in patients of both sexes with hyperPRL at serial sampling, performing a pituitary imaging in all cases of hyperPRL, even if mild, appears to be a cautious choice.
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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 | 0.000 | 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 it