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Record W2783121281 · doi:10.1192/s174936760000391x

Persistent negative symptoms in schizophrenia: survey of Canadian psychiatrists

2013· article· en· W2783121281 on OpenAlexafffundabout
Danyael Lutgens, Martín Lepage, Rahul Manchanda, Ashok Malla

Bibliographic record

VenueInternational Psychiatry · 2013
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsLondon Health Sciences CentreWestern UniversityMcGill UniversityDouglas Mental Health University Institute
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsSchizophrenia (object-oriented programming)PsychiatryMedicinePsychologyClinical psychology

Abstract

fetched live from OpenAlex

A sample of 206 Canadian psychiatrists who routinely treat patients with psychotic disorders were randomly surveyed regarding their knowledge and practice in relation to persistent negative symptoms of schizophrenia. Large majorities reported observing a high prevalence of persistent negative symptoms that do not respond to available treatments (83%), have a profound impact on functional outcomes (96.5%) and contribute to family burden. Almost half the sample (43%) recognised the importance of formally assessing persistent symptoms and nearly a third (30%) indicated that this was a part of their usual practice. These survey results correspond with recent consensus and highlight the importance and challenge of treating persistent negative symptoms in schizophrenia.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.291
Teacher spread0.268 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2013
Admission routes3
Has abstractyes

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