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Record W2154903539 · doi:10.1186/2008-2231-21-30

Methamphetamine-associated psychosis: a new health challenge in Iran

2013· article· en· W2154903539 on OpenAlexaff
Zahra Alam Mehrjerdi, Alasdair M. Barr, Alireza Noroozi

Bibliographic record

VenueDARU Journal of Pharmaceutical Sciences · 2013
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPopularityMedicinePsychiatryRoad mapMedical emergencyPsychologyGeography

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.852
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.168
GPT teacher head0.462
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations38
Published2013
Admission routes1
Has abstractyes

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