Cognitive Impairment in Patients With Depression: Awareness, Assessment, and Management
Bibliographic record
Abstract
Article Abstract †‹†‹ Click to enlarge page Cognitive impairment is a common, often persistent, symptom of major depressive disorder (MDD) that is disproportionately represented in patients who have not returned to full psychosocial functioning. The ultimate goal of treatment in depression is full functional recovery, and assessing patients for cognitive impairment and selecting treatments that address cognitive dysfunction should lead to improved functional outcomes. Unfortunately, many clinicians use screening and assessment tools that are not suited for measuring cognitive impairment in patients with depression. The new THINC-it assessment tool is the first instrument that provides objective and subjective data on dysfunction in all the cognitive domains commonly affected by depression. In regard to treatment, several pharmacologic and nonpharmacologic interventions have been investigated as treatments for cognitive dysfunction in individuals with MDD. However very few studies of treatments for cognitive function in patients with MDD have been adequate, in terms of sample size and study methods, to guide clinical practice. The best evidence supports the moderate efficacy of some antidepressants, cognitive-behavioral therapy, and exercise. From the Department of Family Medicine, Boston University, Massachusetts (Dr Culpepper); the Department of Mood and Anxiety Disorders, The University of British Columbia, Vancouver, Canada (Dr Lam); and the Mood Disorders Psychopharmacology Unit, University of Toronto, Ontario, Canada (Dr McIntyre). †‹†‹†‹†‹
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".