Monitoring of prolactin levels in children and adolescents prescribed antipsychotic medication: a complete audit cycle
Bibliographic record
Abstract
Aims and methods Antipsychotics have proven benefits in children and adolescents with autism spectrum disorders. However, notwithstanding some therapeutic benefits significant side effects are associated with the use of antipsychotics, such as hyperprolactinaemia. We completed an audit cycle between April 2013 and December 2013 to evaluate the practice in the Beechpark Autism Service with respect to monitoring and managing hyperprolactinaemia in children and adolescents prescribed antipsychotics. The re-audit assessed whether the recommended guidelines and changes had been implemented. The National Institute for Health and Care Excellence guidelines were used as a gold standard for this audit. RESULTS: Basal determinations of serum prolactin improved significantly at the end of the audit cycle (28.6% v. 57%) with slight improvement in six monthly repeat prolactin monitoring (28.6% v. 39.1%) showing some change in clinical practice. However, there was minimal improvement in managing hyperprolactinaemia (0% v. 12.5%). Clinical implication There is growing awareness about hyperprolactinaemia associated with the use of antipsychotic medication in children and adolescents and the long-term effects. Clear documented guidelines will help increase and improve the monitoring and management of hyperprolactinaemia in these groups of patients. However, more needs to be done in improving the practice of monitoring and managing hyperprolactinaemia in children and adolescent prescribed antipsychotic medication giving the documented long-term effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".