Executive Dysfunction in Non-Psychotic Unipolar Depressed Patients: Assessement by the Wisconsin (Berg) Card Sorting Test
Bibliographic record
Abstract
Introduction: Alterations in executive functioning are frequent in depressed subjects, being the Wisconsin Card Sorting Test (WCST) one of the most utilized instruments to assess it, even though, when individually compared, this test’s items did not show consistency. Method: This study aimed to compare the performance of a group comprising 36 non-psychotic unipolar depressed patients (23 women and 13 men, with a mean age of 44.28 years old [SD = 14.78]) with 36 healthy controls (22 women and 14 men, with a mean age of 42.22 years old [SD = 15.19]) in a computerized version of WCST. Results: We found significant differences between depressed patients and healthy controls regarding number of categories, perseverative responses, perseverative errors, non-perseverative errors, percentage of conceptual level responses and failure to maintain set, clearly influenced by the variable age, which showed a shared variance between 17% and 33% in depressive patients’ performance and between 16% and 26% in healthy controls’ performance. Conclusions: Results allowed us to identify differences in performance between the two groups, therefore this version of the WCST revealed itself a reliable alternative to assess Executive Functions (EFs), accessible to all clinicians.
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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 | 0.001 | 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".