An Analysis of Potentially Prolactin-Related Adverse Events and Abnormal Prolactin Values in Randomized Clinical Trials with Paliperidone Palmitate
Bibliographic record
Abstract
BACKGROUND: Paliperidone palmitate has been associated with serum prolactin elevations in some patients. However, few individuals with elevated prolactin levels (hyperprolactinemia) have symptomatic potentially prolactin-related adverse events (PPR-AEs). OBJECTIVE: To quantify rates of hyperprolactinemia in subjects treated with the newly marketed paliperidone palmitate long-acting injection (PP-LAI) in randomized clinical trials, summarize rates of PPR-AEs in those trials by sex and dose, and determine how many PPR-AEs required treatment. METHODS: Numbers and rates of investigator-reported hyperprolactinemia and PPR-AEs were obtained from the sponsor's clinical trial database and have been included in regulatory filings. Results were tabulated for males, females, and overall, and by dose administered, using descriptive statistics. Those requiring treatment were described as well. RESULTS: There were 3173 subjects (61.4% males) exposed to PP-LAI in 10 clinical trials; 2831 (89.2%) patients had recorded prolactin levels, including 1759 males (90.3% of exposed males) and 1072 females (87.5% of exposed females). Overall, at any time, prolactin levels were elevated for 38.8% of the subjects (39.5% for males and 37.7% for females; p = 0.354 between sexes). However, there was no significant correlation between monthly dose and proportion of subjects with elevated prolactin levels (p = 0.109). There were 115 PPR-AEs in 107 patients (3.4%); 51 (44.3% of PPR-AEs) cases represented asymptomatic hyperprolactinemia. The remaining 64 symptomatic PPR-AEs affected 2.0% of the total number of subjects. Fifteen events in 13 participants (0.41% of patients or 4.7 events/1000 patients) required treatment. CONCLUSIONS: Clinicians should periodically assess patients on paliperidone palmitate for any PPR-AEs and carefully assess the benefits and risks when managing these effects.
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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 | 0.087 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".