Polypharmacy and psychotropic drug loading in patients with schizophrenia in Asian countries: Fourth survey of Research on Asian Prescription Patterns on antipsychotics
Bibliographic record
Abstract
AIM: The aim of the present study was to survey the prevalence of antipsychotic polypharmacy and combined medication use across 15 Asian countries and areas in 2016. METHODS: By using the results from the fourth survey of Research on Asian Prescription Patterns on antipsychotics, the rates of polypharmacy and combined medication use in each country were analyzed. Daily medications prescribed for the treatment of inpatients or outpatients with schizophrenia, including antipsychotics, mood stabilizers, anxiolytics, hypnotics, and antiparkinson agents, were collected. Fifteen countries from Asia participated in this study. RESULTS: A total of 3744 patients' prescription forms were examined. The prescription patterns differed across these Asian countries, with the highest rate of polypharmacy noted in Vietnam (59.1%) and the lowest in Myanmar (22.0%). Furthermore, the combined use of other medications, expressed as highest and lowest rate, respectively, was as follows: mood stabilizers, China (35.0%) and Bangladesh (1.0%); antidepressants, South Korea (36.6%) and Bangladesh (0%); anxiolytics, Pakistan (55.7%) and Myanmar (8.5%); hypnotics, Japan (61.1%) and, equally, Myanmar (0%) and Sri Lanka (0%); and antiparkinson agents, Bangladesh (87.9%) and Vietnam (10.9%). The average psychotropic drug loading of all patients was 2.01 ± 1.64, with the highest and lowest loadings noted in Japan (4.13 ± 3.13) and Indonesia (1.16 ± 0.68), respectively. CONCLUSION: Differences in psychiatrist training as well as the civil culture and health insurance system of each country may have contributed to the differences in these rates. The concept of drug loading can be applied to other medical fields.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".