Component analysis of verbal fluency in patients with schizophrenia.
Bibliographic record
Abstract
OBJECTIVE: Clustering and-switching components of phonemic fluency performance were compared in patients with schizophrenia and healthy normal controls. BACKGROUND: These components were selected to provide evidence for a specific anatomic locus for the breakdown of language processes or for a multiple-disease model of schizophrenia. METHOD: As part of a larger battery of neuropsychological tests, phonemic fluency tests were administered on an individual basis. On separate 60-second trials, participants were instructed to generate words beginning with the letters C, F, and L, excluding proper names and variants of the same word. Three scores were obtained for each participant: (1) number of words generated, excluding errors and repetitions; (2) mean cluster size; and (3) raw number of switches. RESULTS: The patients showed small but significant impairments in clustering and larger impairments in switching relative to normal controls. CONCLUSIONS: This pattern suggests a relatively greater deficit in functioning in the frontal lobe than in the temporal lobe. However, neither measure was able to completely discriminate patients with schizophrenia from controls. Moreover, differences in fluency performance were observed among subtypes of schizophrenia. Taken together, the findings of impaired performance for both aspects of fluency, differences between subtypes, and the failure to completely discriminate patients with schizophrenia from controls indicate that there is not a single marker of the disease, at least among these fluency variables. Instead, the current findings are more supportive of a multiple-disease model of schizophrenia.
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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 | 0.002 |
| 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 | 0.000 |
| 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".