Application of computerized cognitive test battery in major depressive disorder: a narrative literature review.
Bibliographic record
Abstract
BACKGROUND: Major depressive disorder (MDD) is a chronic and debilitating relapsing-remitting mood disorder, characterized by psychological, cognitive, and behavioral disturbances. The assessment of cognitive dysfunction in individuals with MDD has increasingly become a topic of concern in recent years. AIMS: To pool and compare the characteristics of various cognition evaluation tools. METHOD: Overview of recent research in application of computerized cognitive test battery in MDD. RESULTS: With recent technological advances in mobile health technologies and the ubiquity of smartphones, the use of traditional tools is no longer sufficient to monitor the dynamic changes of an individual's cognitive performance, which may be influenced by many factors, including, but not limited to, disease course and medications. Computerized tests have many advantages over traditional neuropsychological testing, chiefly in terms of time and cost savings, accurate recording of multiple response components, and the ability to automatically store and compare performance between testing sessions. In the following review, we summarized cognitive impairment characteristics of MDD, introduced traditional assessment tools of cognitive function in MDD, and reviewed the development of the current computerized cognitive test batteries for MDD. The comparisons among cognitive function evaluation tools were also performed. CONCLUSIONS: It is our belief that the improvement of existing novel computerized cognitive test batteries, the development of more comprehensive and easy-to-operate scales, verification techniques and multiple follow-up surveys among large sample populations may provide valuable clues for the evaluation and tracking of cognitive function in individuals with MDD.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".