F101. LINGUISTIC DETERMINANTS OF FORMAL THOUGHT DISORDER IN FIRST EPISODE PSYCHOSIS
Bibliographic record
Abstract
In addition to positive and negative symptoms, patients with schizophrenia have notable disturbances in their ability to produce integrated, complex thoughts. Clinical quantification of thought disorder is a challenging process, contributing to the paucity of mechanistic understanding of disorganization seen in early stages of psychosis. We studied the linguistic aspects of clinically quantified dimensions of thought disorder – especially the dimension of disorganized thinking, in a sample of patients with untreated, first episode psychosis and healthy controls. Data were collected from a sample of antipsychotic-naïve FEPs (n=37) and group matched HCs(n=24) as part of a longitudinal clinical study. Participants were recruited from the Prevention and Early Intervention Program for Psychoses in London, Ontario, Canada (PEPP-London). After completing a comprehensive assessment of clinical symptoms, participants were administered the Thought Language Index (TLI) in which one minute of speech was induced using pictures from Thematic Apperception Test. Analyses of linguistic characteristics were conducted using Coh-Metrix, a software system that analyzes written and transcribed speech samples for various linguistic characteristics. Patients and controls were compared on levels of (1) Syntactic Complexity (2) Syntactic categories [word information] (3) Connectives and (4) Lexical Diversity after controlling for total words spoken, parental socioeconomic status, and gender. A single metric for each of these four measures were derived using principal component analyses applied to individual items of CohMetrix from each of these 4 aspects of speech. All 4 measures were used to predict the scores of TLI Disorganization. Compared to HCs, FEPs showed higher overall syntactic complexity (F=6.7, p=0.012), indicating an increase in the overall linguistic complexity of FEP speech samples. FEPs also showed abnormalities in the incidence of syntactic categories (F=5.1, p =0.028), especially a higher use of the first-person singular pronoun. Patients and controls did not differ significantly on the use of connectives and lexical diversity. Within the patient sample, TLI disorganization was predicted by excessive use of connectives (F=9.8, p=0.005), indicating a strong role for inappropriate cohesive links forged between ideas as a cardinal feature of disorganized thinking. To our knowledge, the higher syntactic complexity and abnormal employment of syntactic categories among patients is a novel finding in untreated psychosis. The construct of disorganization, measured using clinical judgment, likely relates to the excessive use of connectives, likely contributing to the clinical intuition of incoherence. In early stages of active psychosis, formal thought disorder could be quantified more reliably using automated syntax analysis.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".