Audit of inpatient and outpatient paliperidone palmitate injection prescribing practices
Bibliographic record
Abstract
Abstract Background Paliperidone palmitate is marketed as a once‐monthly long‐acting antipsychotic injection and is expected to provide an avenue for long‐term control of symptoms in patients with schizophrenia. Although marketed as once monthly medication, some medication references recommend a dosing interval of every 4 weeks for maintenance doses. Aim (a) To assess whether the prescribing/administration practices relating to the dosing and dosing interval of paliperidone palmitate are consistent with recommendations outlined in major medication references in Australia and (b) to assess the number of doses and any differences in costs associated with different dosing intervals. Method A retrospective audit of doses of paliperidone palmitate administered in a regional mental health service in Queensland between 1 October 2011 and 30 October 2013. Results Twenty‐five of the twenty‐nine patients who had initiation doses prescribed for days 1 and 8 received the recommended initiation regimen. Sixteen percent of first monthly doses administered to patients were the recommended 75 mg strength; 60% of maintenance doses were administered ≤28 days after the previous dose, and 78% of patients studied for a year or more received at least 13 doses in 1 year, rather than 12 doses. Conclusion A dosing interval of every 4 weeks resulted in at least one extra injection per patient per year, with associated cost. There is a need for a consensus on the dosing interval of paliperidone palmitate so that there is consistency in the number of doses received per patient, per year.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".