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Record W2763971560 · doi:10.7759/cureus.1772

Ethnic and Age Disparities in Patients Taking Long-acting Injectable Atypical Antipsychotics

2017· article· en· W2763971560 on OpenAlexaff
Mateen Soleman, Nikki HT Lam, Benjamin K.P. Woo

Bibliographic record

VenueCureus · 2017
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineEthnic groupPsychiatryPediatrics

Abstract

fetched live from OpenAlex

Introduction This study will determine whether different ethnicities and different age groups receive equal amounts of long-acting atypical antipsychotics in comparison to their oral equivalents. Methods Secondary analyses of data from the Los Angeles County Department of Health Services Electronic Health Record (total N=63,134 inpatient visits) were performed. Chi-squared statistics were used to compare ethnicity and age with the use of either risperidone injectable or paliperidone palmitate (r-LAIs) versus risperidone oral. Results Among the 63,134 total inpatient visits, there were 3,011 patient visits that included the use of an atypical antipsychotic. Of these 3,011 visits, 452 (15.0%) were on r-LAIs and 2,559 (85.0%) were on risperidone oral. No statistically significant disparities were identified with the use of r-LAIs as compared to oral risperidone amongst ethnic groups (chi-square = 0.88, df = 3, p = 0.831). However, there was a statistically significant difference with the use of r-LAIs as compared to oral Risperidone amongst age groups, favoring younger patients (chi-square = 13.46, df = 3, p < 0.004). Conclusion Our data indicate a lack of ethnic disparities in prescribing long-acting atypical antipsychotics and an increased percentage of younger patients being treated with atypical depot antipsychotics over their oral equivalents.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

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

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 head0.080
GPT teacher head0.376
Teacher spread0.296 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2017
Admission routes1
Has abstractyes

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