One-year Outcome of First vs. Later Episode Schizophrenia: A Real-world Naturalistic Study
Bibliographic record
Abstract
OBJECTIVE: The aim the study was to calculate remission, recovery and relapse rates in first episode patients with schizophrenia (FES) vs. patients at a later phase (non-FES). METHODS: Thirty-two FES and 101 non-FES patients took part in the study. The assessment included testing at baseline and at 1 year with the Positive and Negative Syndrome Scale (PANSS), Calgary Depression scale, State-Trait Anxiety Inventory (STAI), Udvalg for Kliniske Undersøgelser (UKU) scale, Simpson Angus, and General Assessment of Functioning (GAF) subscale. The statistical analysis included chi-square test and analysis of covariance. RESULTS: At baseline 15.62% FES vs. 10.89% non-FES patients were in remission; none of FES vs. 2.97% non-FES patients were in recovery. At endpoint, the respective figures were 12.50% vs. 25.00% and 3.12% vs. 3.96%. None of the differences in rates was significant between the two groups except from the percentage of patients being under medication (higher in the non-FES group). Baseline PANSS negative subscale (PANSS-N) was the only predictor of the outcome at endpoint. CONCLUSION: The current study reported very low rates of remission and recovery of patients with schizophrenia without any differences between FES and non-FES patients. One possibility is that the increased antipsychotic treatment compensates for the worsening of the illness with time. An accumulating beneficial effect of antipsychotic treatment suggested that early lack of remission is not prognostic of a poor outcome.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".