Methamphetamine-associated psychosis: a new health challenge in Iran
Bibliographic record
Abstract
The rapidly growing popularity of methamphetamine use in Iran has posed a new health challenge to the Iranian health sector. Methamphetamine-associated psychosis (MAP) has been frequently reported in Iran in recent years. Although methamphetamine use and MAP are considerable health problems in Iran but there is still a need to conduct epidemiological studies on the prevalence of MAP and its health-related problems. The present paper emphasizes that health policy makers should consider the immediate needs of drug users, their families and the community to be informed about the detrimental health effects associated with MAP. Although MAP could be managed by prescribing benzodiazepines and psychiatric medications but the most effective regime for stabilizing patients with MAP still needs to be studied in Iran. Constant collaborations among psychiatric services and outpatient psychotherapeutic services should be established to successfully manage MAP in Iran. Iranian clinicians especially emergency medicine specialists should be informed about the differences between the two forms of transient and recurrent MAP in order to implement appropriate pharmacological therapies to manage MAP. It is hoped that special training courses are designed and implemented by health policy makers to inform clinicians, health providers and especially emergency medicine specialists to effectively deal with MAP.
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 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 | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".