Signs and symptoms in the pre-psychotic phase: description and implications for diagnostic trajectories
Bibliographic record
Abstract
BACKGROUND: Few studies have examined the underlying factor structure of signs and symptoms occurring before the first psychotic episode. Our objective was to determine whether factors derived from early signs and symptoms are differentially associated with non-affective versus affective psychosis. METHOD: A principal components factor analysis was performed on early signs and symptoms reported by 128 individuals with first-episode psychosis. Factor scores were examined for their associations with duration of untreated illness, drug abuse prior to onset of psychosis, and diagnosis (schizophrenia versus affective psychosis). RESULTS: Of the 27 early signs and symptoms reported by patients, depression and anxiety were the most frequent. Five factors were identified based on these early signs and symptoms: depression, disorganization/mania, positive symptoms, negative symptoms and social withdrawal. Longer duration of untreated illness was associated with higher levels of depression and social withdrawal. Individuals with a history of drug abuse prior to the onset of psychosis scored higher on pre-psychotic depression and negative symptoms. The two mood-related factors, depression and disorganization/mania, distinguished the eventual first-episode diagnosis of affective psychosis from schizophrenia. Individuals with affective psychosis were also more likely to have a 'mood-related' sign and symptom as their first psychiatric change than individuals later diagnosed with schizophrenia. CONCLUSIONS: Factors derived from early signs and symptoms reported by a full diagnostic spectrum sample of psychosis can have implications for future diagnostic trajectories. The findings are a step forward in the process of understanding and characterizing clinically important phenomena to be observed prior to the onset of psychosis.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".