Exploring delusional themes and other symptoms in first episode psychosis: A network analysis over two timepoints
Bibliographic record
Abstract
Delusions are a defining feature of psychosis and play an important role in the conceptualization and diagnosis of psychotic disorders; however, the particular role that different delusions play in the prognosis of these disorders is not well understood. This study explored relationships between delusions and other symptoms in 674 first episode psychosis (FEP) individuals by comparing symptom networks between baseline and 12 months after intake to an early intervention service. Specifically, we (1) estimated regularized partial correlation networks at baseline and month 12, (2) identified the most central symptoms in each network, (3) identified clusters of highly connected symptoms, and (4) compared networks to examine changes in structure and connectivity. At baseline, the most central symptoms were depression, delusions of mind reading, and delusions of thought insertion. At month 12, they were hallucinations, persecutory delusions, and delusions of thought insertion. A symptom cluster was identified at both timepoints comprising of five delusions corresponding to passivity experiences. While network structures did not differ significantly, the month 12 network was significantly more highly connected. Our study captures a shift in illness trajectory over time, wherein transdiagnostic symptomatology at baseline becomes more consolidated around psychotic symptoms by month 12.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".