Long-Term Outcome of Patients with Schizophrenia: A Review
Bibliographic record
Abstract
OBJECTIVE: To review empirical studies that assess outcome of patients with schizophrenia and evaluate the degree to which reported outcome is affected by research methodology, treatment variables, prognostic factors, epidemiologic factors, and patient resilience. METHOD: We reviewed studies that used control subjects and lasted for a decade or more, comparing them with respect to research methodology and choice of outcome variables. RESULTS: Like other mental illnesses and medical illness in general, the natural course of schizophrenia showed itself to have a threefold division of mild, moderate, and severe. Although a great deal of variance in outcome occurred across the studies reviewed, schizophrenia is nevertheless a disorder with relatively poor outcome. Patients with schizophrenia consistently showed poorer courses and outcomes than patients with other psychotic and nonpsychotic psychiatric disorders. On the positive side, subgroups of schizophrenia patients had extended periods of recovery-some without the benefit of extensive mental health aftercare treatment-and patients with schizophrenia did not show a progressive downhill course. CONCLUSION: While documenting the heterogeneity in outcome and the generally poorer outcomes of patients with schizophrenia, the studies reviewed also alert us to the danger of suicide and early death in schizophrenia. In addition, they expose problems in clinical management and treatment and also help us anticipate the possibility of intervals or periods of recovery, some of which appear spontaneously and may be tied to individual patient factors such as resilience.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".