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Record W2102285185 · doi:10.1017/s0033291712000177

Long-term course and outcome in schizophrenia: a 34-year follow-up study in Alberta, Canada

2012· article· en· W2102285185 on OpenAlexaffabout
Stephen C. Newman, Roger Bland, A. Thompson

Bibliographic record

VenuePsychological Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsInstitute of Health EconomicsUniversity of Alberta
Fundersnot available
KeywordsSchizophrenia (object-oriented programming)CohortPsychiatryMedicineCohort studyLife course approachRetrospective cohort studyPediatricsPsychologyInternal medicineDevelopmental psychology

Abstract

fetched live from OpenAlex

BACKGROUND: The aim of this study was to extend an earlier retrospective cohort study of schizophrenia via a prospective study to a follow-up of 34 years, with an emphasis on describing the life-course of the illness. METHOD: Subjects were 128 first-ever admissions for schizophrenia in 1963 to either of two mental hospital in Alberta, Canada. Follow-up continued until death or 1997. A symptom severity scale, with scores ranging from 0 (no symptoms) to 3 (hospitalized), was used to collect time-series data on each subject and create life-course curves. Indices were constructed to summarize the information in each curve. Information on social functioning was also collected. RESULTS: Results were similar for men and women. The life-course curves showed marked variability of symptom severity across subjects and over time. The average score over the entire period of follow-up for the cohort indicated 'moderate' symptoms, and the change in average score from beginning to end of follow-up demonstrated a slight worsening of symptoms. The measures of social functioning indicated that only about one quarter of the patients had a good to excellent outcome. CONCLUSIONS: The long-term course in schizophrenia is one of varying symptom severity, and for many patients, there is a poor overall outcome.

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)0.001
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 score0.568
Threshold uncertainty score0.830

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.057
GPT teacher head0.381
Teacher spread0.325 · 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

Citations24
Published2012
Admission routes2
Has abstractyes

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