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Record W6940740975 · doi:10.11575/prism/41433

Improvement of functioning in patients with schizophrenia: real-world effectiveness of aripiprazole once-monthly (REACT study)

2023· other· en· W6940740975 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Calgary · 2023
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsObservational studyGlobal Assessment of FunctioningAripiprazoleAntipsychoticSchizophrenia (object-oriented programming)Meta-analysisRandomized controlled trialDisease

Abstract

fetched live from OpenAlex

Abstract Background Functional impairment affects many patients with schizophrenia. Treatment with the long-acting injectable antipsychotic aripiprazole once-monthly (AOM) may help improve functioning. Objectives To explore changes in functioning in patients with schizophrenia who received AOM treatment in observational studies. Methods Here we report functional outcomes in the form of Global Assessment of Functioning (GAF) scores in a pooled analysis of data from two observational studies from Canada (NCT02131415) and Germany (vfa non-interventional studies registry 15960N). Data from 396 patients were analyzed. Results At baseline, the mean GAF score was 47.7 (SD 13.4). During 6 months of treatment with AOM, the mean GAF score increased to 59.4 (SD 15.8). Subgroups stratified by patient age (≤35 years/>35 years), sex, disease duration (≤5 years/>5 years) and disease severity at baseline had all significantly improved their GAF at month 6. 51.5% of the patients showed a GAF score increase of at least 10 points, which was regarded as clinically meaningful, and were considered responders. Conclusions These data show that treatment with AOM may help improve patient functioning in a routine treatment setting. Trial registration NCT02131415 (May 6, 2014), vfa non-interventional studies registry 15960N.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score1.000

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)0.0010.000

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 head0.005
GPT teacher head0.176
Teacher spread0.171 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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