Progress of negative symptoms over the initial 5 years of a first episode of psychosis
Bibliographic record
Abstract
BACKGROUND: Specialized early intervention (EI) following a first episode of psychosis (FEP) are effective at reducing negative symptoms, although its trajectory warrants systematic assessment. However, findings are equivocal as to whether extended gains are made post 2 years of EI and whether there is additional benefit of extending EI for an additional 3 years. METHODS: Data on 178 FEP patients, from a randomized controlled trial of a 3-year extension of EI service v. transfer to regular care following 2 years of EI service, were used for this report. Repeated measures analysis of variance were conducted separately for the initial 2 years of treatment in an EI service, and for the 3-year post-randomization to examine trajectories of negative symptoms over the two periods in the two arms of the study. RESULTS: There were significant improvements in total negative symptoms over the first 2 years of EI F(4.612, 797.905) = 25.263, p < 0.001 and in domains of 'expressivity' and 'motivation'. In the following 3 years, there were further significant improvements in negative symptoms F(4.318, 759.908) = 4.182, p = 0.002 with no difference between groups F(4.318, 759.908) = 1.073, p = 0.371. Changes in negative symptoms over the extension period were driven by expressivity F(4.01, 674.73) = 7.19, p < 0.01, but not motivation F(6.58, 1112.18) = 0.95, p = 0.46. CONCLUSION: Negative symptoms improve significantly over the first 2 years of EI. Subsequent amelioration was largely the result of expressivity. Motivation deficits remained stable. Extended EI offered no advantage over regular care post-randomization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".