Top-Down Computerized Cognitive Remediation in Schizophrenia: A Case Study of an Individual with Impairment in Verbal Fluency
Bibliographic record
Abstract
The objective of this case study was to assess the specific effect of cognitive remediation for schizophrenia on the pattern of cognitive impairments. Case A is a 33-year-old man with a schizophrenia diagnosis and impairments in visual memory, inhibition, problem solving, and verbal fluency. He was provided with a therapist delivered cognitive remediation program involving practice and strategy which was designed to train attention, memory, executive functioning, visual-perceptual processing, and metacognitive skills. Neuropsychological and clinical assessments were administered at baseline and after three months of treatment. At posttest assessment, Case A had improved significantly on targeted (visual memory and problem solving) and nontargeted (verbal fluency) cognitive processes. The results of the current case study suggest that (1) it is possible to improve specific cognitive processes with targeted exercises, as seen by the improvement in visual memory due to training exercises targeting this cognitive domain; (2) cognitive remediation can produce improvements in cognitive processes not targeted during remediation since verbal fluency was improved while there was no training exercise on this specific cognitive process; and (3) including learning strategies in cognitive remediation increases the value of the approach and enhances participant improvement, possibly because strategies using verbalization can lead to improvement in verbal fluency even if it was not practiced.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".