Does ketamine mimic aspects of schizophrenic speech?
Bibliographic record
Abstract
Speech disturbances are well-known symptoms contributing to the diagnosis of schizophrenia. Subanesthetic doses of the N-methyl-D-aspartate (NMDA) antagonist ketamine have been reported to produce positive and negative symptoms and cognitive impairments consistent with those seen in schizophrenia. Insofar as this is true, it constitutes evidence that the NMDA system is involved in schizophrenia. It is therefore of interest to know whether ketamine produces speech disturbances like those of schizophrenia. Quantitative computer-aided analysis of apparently normal speech can detect clinically relevant changes and differences that are not noticeable to the human observer. Accordingly, in this study, speech samples were analysed for repetitiousness, idea density, and verb density using software developed by the authors. The samples came from two experiments, a within-subjects study of healthy volunteers given intravenous ketamine versus placebo, and a between-groups study of patients diagnosed with schizophrenia and comparable healthy controls.Our primary hypothesis was that in both schizophrenia and ketamine, repetitiousness would increase, since perserverative speech is a well-known symptom of schizophrenia. Our secondary hypotheses were that in both schizophrenia and ketamine, idea density and verb density would decrease as indicators of cognitive impairment. The primary hypothesis was confirmed in the schizophrenia experiment (between groups) and the ketamine experiment (within subjects). The secondary hypotheses were disconfirmed except that in the ketamine experiment, verb density was significantly lowered. Reduced use of verbs apparently reflects a cognitive impairment of a different type than repetitiousness, and further investigation is needed to determine whether this impairment occurs in 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".