Pragmatic Language and Schizophrenia: Interpretation of Metaphors
Bibliographic record
Abstract
ABSTRACT Background: Schizophrenia is a severe and chronic psychiatric disorder with significant cognitive deficits, which are considered structural markers for the disease. Language disturbances have an important role in patients' social functioning and interpersonal relationships. Objectives: Evaluate the capacity to understand pragmatic language in schizophrenic patients, through the comprehension of non-literal meaning in metaphors and the ability to use contextual clues to better understand their meanings. Methods: Thirty patients were evaluated using Abbreviated Intelligence Scale (WASI), Interpretation of Metaphors subtest of the Montreal Communication Evaluation Battery (MAC). Results: The linear regression model showed that schizophrenic patients presented below average performance in the interpretation of metaphors task, with tendency to concrete interpretations. Variables such as IQ, WASI Vocabulary subtest and years since onset influenced the patients' pragmatic language skills. This relation was not found for family history. Existence of the metaphor in native colloquial language (Portuguese) and being given alternatives to choose from, enhanced patients' performance. Discussion: Results corroborate findings regarding this population's difficulties in the language cognitive domain. Development of interventions aiming comprehension of pragmatic language could help ease patients' social difficulties, especially if started early at onset. Also, better understanding of this deficit can help create rehabilitation strategies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".