A systematic review of longitudinal outcome studies of first-episode psychosis
Bibliographic record
Abstract
BACKGROUND: Existing outcome literature has had an over-representation of chronic patients and suggested a progressive course and poor outcome for schizophrenia. The current study aimed to recombine data of samples from longitudinal studies of first-episode psychosis (FEP) to describe outcome and its predictors. METHOD: A literature search (1966-2003) was conducted for prospective studies examining outcome in first-episode non-affective psychosis using the following key words: early, first, incident, episode, admission, contact, psychosis, schizophrenia, psychotic disorders, course, outcome, follow-up, longitudinal, cohort. These were pooled and analyzed using descriptive and regression analyses. RESULTS: Thirty-seven studies met the inclusion criteria, representing 4100 patients with a mean follow-up of 35.1+/-6.0 months. Studies varied in the categories of outcome used, the most common being 'good' (54% of studies) and 'poor' (34% of studies), variably defined. In studies reporting these categories, good outcomes were reported in 42.2% (3.5%) and poor outcomes in 27.1% (2.8%) of cases. Predictors associated with better outcome domains were: combination of pharmacotherapy and psychosocial therapy, lack of epidemiologic representativeness of the sample, and a developing country of origin. Use of typical neuroleptics was associated with worse outcome. Stratification analyses suggested that populations with schizophrenia only, and those with prospective design, were associated with worse outcome domains. CONCLUSIONS: Outcome from FEP may be more favorable than previously reported, and treatment and methodological variables may be important contributors to outcome. Significant heterogeneity in definitions and methodology limited the comparison and pooling of data. A multi-dimensional, globally used definition of outcome is required for future research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".