O3.5. EARLY TRAJECTORIES OF POSITIVE SYMPTOMS REMISSION IN FIRST EPISODE-PSYCHOSIS: A 2-YEAR FOLLOW-UP STUDY
Bibliographic record
Abstract
Abstract Background The Prevention and Early intervention Program for Psychosis (PEPP) provides young people with first episode psychosis (FEP) rapid access to appropriate mental health services designed on the principles of early intervention (EI). We have previously demonstrated high rates of positive symptom (PS) remission. However, the relationship between PS, negative symptoms (NS) and functional outcomes remains unclear. Adherence to medication and early treatment response have been shown to be important independent determinants of the level of, and time to, symptom and functional remission, respectively. While trajectories of symptom severity have been shown to be heterogeneous, no previous study has investigated the prognosis of PS remission among individuals with FEP treated in an EI service. Identification of different trajectories of PS remission is a useful strategy to provide insight into clinically meaningful subgroups of patients while providing valuable information on NS and functioning for improving treatment outcomes. Methods The 2-year treatment at PEPP comprises different psychosocial (i.e., cognitive behavioral therapy, group intervention, family intervention, individual placement and support program) and psychopharmacological interventions (i.e., minimum effective dosage of second-generation antipsychotics). Monthly assessments were conducted from baseline to month 24. A total of 387 FEP patients, aged 14–35 years, with DSM IV affective or non-affective psychosis and little or no prior antipsychotic treatment (i.e., < 30 days) were included. PS remission was defined as absence of overt psychotic symptoms (i.e., all global SAPS items ≤ 2). A Latent Class Growth Analysis (LCGA) was used to investigate the distinct trajectories based on cumulative length of PS remission assessed at 3, 6, 9, 12, 15, 18, 21, and 24 months of treatment. Predictors of trajectories were investigated among sociodemographic, pre-treatment, as well as baseline and course clinical characteristics. Chi-square tests, one-way and mixed ANOVAs identified which baseline and longitudinal variables differed between and within trajectories. Candidate predictors that were statistically significant were then entered into a multinomial regression model to determine which factors independently predict trajectory membership. Results Three distinct trajectories of PS remission were identified. Excellent (68%), unstable (15%) and poor (17%) trajectory. Trajectories differed at baseline in DUP, diagnosis of affective psychosis and PS severity. Over the 24 months of treatment, negative, depressive, anxiety and mania symptoms, as well as functioning, best improved in the excellent trajectory among which patients were prescribed less antipsychotics in term of chlorpromazine equivalent than patients in other trajectories. Multinomial regression of baseline characteristics revealed that absence of early treatment response at 3 months (adjusted OR=2.53; 95%CI=1.24–5.16) independently predicted poorer trajectory. Discussion These results highlight the heterogeneous prognosis of PS remission suggesting that the diversity in FEP response and phenotypes may be determined by different pathophysiological underpinnings. The fact that early response was found to be a strong predictor of PS remission supports early and adequate symptom control for which medication is a critical issue. Further research applying data-driven trajectory analysis in FEP is warranted to facilitate better characterization of longer-term patterns of remission and development of targeted intervention to promote early recovery.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".