MétaCan
Menu
Back to cohort
Record W2101130507 · doi:10.1192/bjp.bp.107.040410

Cognitive markers of short-term clinical outcome in first-episode psychosis

2008· article· en· W2101130507 on OpenAlexafffund
Michael Bodnar, Ashok Malla, Ridha Joober, Martín Lepage

Bibliographic record

VenueThe British Journal of Psychiatry · 2008
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsPsychosisOutcome (game theory)CognitionPsychiatryPsychologyClinical psychologyMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Outcome from psychotic disorders is heterogeneous with poorer outcomes frequently identified too late to be influenced. Symptomatic ratings at 1 or more years following initiation of treatment have been related to cognition in first-episode psychosis. However, the relationship between cognition and early outcome remains unclear. AIMS: To determine whether specific cognitive domains could identify poor short-term outcome among individuals with first-episode psychosis. METHOD: One hundred and fifty-one individuals with first-episode psychosis were divided into two groups based on 6-month clinical data after the initiation of treatment. Six cognitive domains were compared among 78 participants with poor outcomes, 73 with good outcomes and 31 healthy controls. RESULTS: Lower performance on verbal memory (z-scores: poor outcome=-1.3 (s.d.=1.1); good outcome=-0.8 (s.d.=0.9); P=0.001) and working memory (poor outcome=-1.0 (s.d.=1.2); good outcome=-0.4 (s.d.=0.9); P=0.003) identified individuals with first-episode psychosis with a poor outcome after 6 months of treatment. CONCLUSIONS: The early identification of those individuals with first-episode psychosis with a poor clinical outcome may encourage clinicians to pay special attention to them in the form of alternative pharmacological and psychological treatments for a more favourable outcome in the long term.

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)0.001
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 score0.018
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.055
GPT teacher head0.372
Teacher spread0.316 · 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

Citations52
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueThe British Journal of PsychiatrySame topicSchizophrenia research and treatmentFrench-language works237,207