Polypharmacy and psychotropic drug loading in patients with schizophrenia in Asian countries: Fourth survey of Research on Asian Prescription Patterns on antipsychotics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it