Osteoporotic fractures in patients with untreated hyperprolactinemia vs. those taking dopamine agonists: A systematic review and meta-analysis.
Bibliographic record
Abstract
OBJECTIVE: Hyperprolactinemia is associated with bone fragility. Traditionally attributed to prolactin-induced hypogonadism, recent studies have identified increased fracture rates independent of gonadal function. METHODS: We performed a systematic review to identify studies assessing fracture risk in patients with untreated hyperprolactinemia compared to those on dopamine agonists. MEDLINE, EMBASE, Cochrane, Web of Science and BIOSIS Previews databases were searched from inception to December 2013 for studies of hyperprolactinemia with fractures as an outcome. Two authors independently performed title and abstract searches, full-text searches, data abstraction, and quality assessment. A summary odds ratio (OR) was calculated using a random effects model. RESULTS: Of the 197 articles identified, 2 met inclusion criteria. Both cross-sectional studies examined cabergoline use (or non-use) in patients with prolactin-secreting adenomas, with vertebral fractures as the primary outcome. For women, vertebral fractures were identified in 46% of untreated patients, vs. 20% of patients on cabergoline (OR: 0.29, 95% CI: 0.10-0.78). For men, the results were 67% in untreated, vs. 26% in cabergoline treated patients (OR: 0.18, CI: 0.03-0.94), with no difference between gonadal and hypogonadal men (p=0.8). Combining studies gave a summary odds ratio of 0.25 (CI: 0.11-0.59), I2=0%. CONCLUSIONS: In the limited studies available, fracture prevalence was increased in patients with untreated hyperprolactinemia compared to those on treatment, independent of gonadal function. Further studies are needed to clarify if post-menopausal women, or high-risk men, with no other indication for treatment, should be on dopamine agonists to decrease fracture risk.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.034 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".