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Record W2939015561 · doi:10.1093/schbul/sbz020.575

S30. COMBINING PHARMACOTHERAPY OF BI 425809 WITH COMPUTERIZED COGNITIVE TRAINING IN PATIENTS WITH SCHIZOPHRENIA: A RANDOMIZED TRIAL METHODOLOGY

2019· article· en· W2939015561 on OpenAlexaff
Sean McDonald, Jana Podhorná, Songqiao Huang, Philip D. Harvey

Bibliographic record

VenueSchizophrenia Bulletin · 2019
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsBoehringer Ingelheim (Canada)
Fundersnot available
KeywordsNeurocognitiveSchizophrenia (object-oriented programming)CognitionCognitive trainingRandomized controlled trialMedicinePlaceboNeuroplasticityEffects of sleep deprivation on cognitive performancePsychologyPsychiatryPhysical medicine and rehabilitationInternal medicine

Abstract

fetched live from OpenAlex

Trials of pharmacotherapies targeting cognition in schizophrenia have produced mainly negative results, and there are no approved cognition-enhancing pharmacological treatments available for patients with schizophrenia. This may be due in part to varying levels of concurrent cognitive stimulation, particularly for pharmacotherapies targeting neuroplasticity, which is activity-dependent (i.e. responds to cognitive demand). Often, the surroundings and environment of patients with schizophrenia provide only a low level of cognitive demand. At-home computerized cognitive training (CCT) can be one way to increase the level of environmental cognitive stimulation for these patients. BI 425809, a glycine transporter 1 inhibitor, increases glycine in the synaptic cleft and thus should lead to improved glutamatergic neurotransmission, synaptic neuroplasticity, and cognition. We describe a large randomized multicenter trial conducted across several countries that aims to explore whether enhanced cognitive stimulation through at-home CCT combined with BI 425809 pharmacotherapy could significantly improve cognition in patients with schizophrenia. This double-blind, parallel group trial plans to recruit patients with schizophrenia on stable antipsychotic therapy, across approximately 40 centers in 7 countries. We plan to randomize 200 patients who are compliant with CCT during a 2-week run-in period in a 1:1 ratio to either BI 425809 or placebo once daily for 12 weeks. The at home CCT should be optimally performed across 3–5 sessions (approximately 2.5 hours in total) per week. The primary endpoint will be change from baseline in neurocognitive function, measured by the neurocognitive composite score of the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery (MCCB) after 12 weeks of treatment. The neurocognitive composite score was selected specifically because it excludes the social cognition domain, which is typically targeted by separate training procedures. Novel exploratory endpoints include the Virtual Reality Functional Capacity Assessment Tool (VRFCAT) to assess skills for daily functioning and the Balloon Effort task to assess the role of motivation in cognitive performance and also in patients’ willingness to comply with at-home CCT. Scores from the Patient Reported Experience of Cognitive Impairment in Schizophrenia (PRECIS), which is being developed to assess patients’ subjective experiences of cognitive impairment in schizophrenia, will also be assessed in a subset of patients. Initiation of the trial is planned for February 2019 and the last patient out is planned for December 2020. Results are expected in early 2021. This trial is critical to the field for several reasons. Firstly, the results will show if there is an enhanced benefit to combining pharmacotherapy with increased cognitive stimulation through at-home CCT in patients with schizophrenia. Second, the trial will evaluate the impact of BI 425809 with adjunctive CCT on outcomes relating to patients’ daily functioning, including the novel virtual reality test, VRFCAT. Third, the role of motivation in cognition, CCT compliance, and CCT performance will be explored. Finally, this trial will demonstrate if at-home CCT can be effectively implemented in a large trial across many centers and several countries. One key strength of this trial is the relatively large sample size which should result in robust data. Funding: Boehringer Ingelheim International GmbH (1346.38)

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.037
GPT teacher head0.276
Teacher spread0.238 · 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 designRandomized trial
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

Citations2
Published2019
Admission routes1
Has abstractyes

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