Withdrawal of dopamine agonist treatment in patients with hyperprolactinaemia: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Objective To estimate the proportion of patients with persistent normoprolactinaemia following dopamine agonist (DA) withdrawal and to identify predictors of successful withdrawal in patients with hyperprolactinaemia. Design, patients, and measurements A systematic review of observational eligible studies were identified by searching PubMed and Embase. The primary outcome was the proportion of patients with normoprolactinaemia after cessation of DA treatment. Secondary outcome included the proportion of patients with normoprolactinaemia after DA withdrawal using individual patient data. Risk of bias was assessed by using Newcastle‐Ottawa Scale. Pooled proportions were estimated using a random effects model in case I 2 ≤ 75% or by reporting range of effects if I 2 > 75%. Results Thirty‐two observational studies enroling 1563 patients were included. The proportion of patients with persistent normoprolactinaemia ranged from 0% to 75% ( I 2 = 84%). Heterogeneity was partly explained by age with more successful withdrawal in patients of higher age. Individual patient data analyses suggested that the proportion of patients with persistent normoprolactinaemia 6 months after DA withdrawal with a low maintenance dose and full regression of the prolactinoma was 87.7% (95% confidence interval [CI] = 60.7–97.1; I 2 = 0%) and 58.4% (95% CI = 23.8–86.3; I 2 = 75%) for microadenomas and macroadenomas, respectively. Conclusions The proportion of patients with persistent normoprolactinaemia following DA withdrawal treatment varied greatly, partly explained by the mean age of participants of the individual studies. Individual patient data analysis suggested that successful withdrawal was likely in patients with full regression of prolactinomas using a low maintenance dose before cessation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.002 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".