Linguistic Determinants of Formal Thought Disorder in First Episode Psychosis
Bibliographic record
Abstract
Background: Disturbances in the expression of thought is a core feature of schizophrenia but assessment of disordered thinking is challenging, relying on clinical intuition which may contribute to the paucity of mechanistic understanding of disorganization seen in early stages of psychosis. We studied the use of linguistic connectives in relation to clinically quantified dimensions of thought disorder using automated speech analysis in untreated, first episode psychosis (FEPs) and healthy controls (HCs)Methods: Data were collected from 39 treatment-naïve, actively psychotic first episode patients (FEPs) recruited on first contact and 23 group matched healthy controls. Three one-minute speech samples were induced in response to photographs from the Thematic Apperception Test and speech was analyzed using COH-METRIX software. Five connectives variables from the Coh-Metrix software were reduced using principle component analysis, resulting in two linguistic connectives factors. Thought disorder was assessed using the Thought Language Index (TLI) and the PANSS-8. Results: linguistic connective factors predicted disorganized thought, but not impoverishment suggesting aberrant use of connectives is specific to positive thought disorder. Factor 2 (increased temporal, reduced logical connectives), showed statistically significant main effects (F[2,56]=5.58, p=0.006) on ANOVA among HCs, low- and high-disorganization FEPs. Post-hoc differences were noted between High- and Low- disorganization FEPs (p=0.004). Factor 2 was not correlated with measures of disease severity or cognition suggesting connective use is a specific index of disorganized thinking rather than overall illness status.Conclusions: Disorganization in psychosis, assessed on the basis of clinical judgement, is likely linked to the excessive or inappropriate use of linguistic connectives leading to an intuitive sense of incoherence to the observer. In the early stages of untreated psychosis, thought disorder may be quantifiable 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".