Cognitive dysfunction in major depression and bipolar disorder: <scp>A</scp>ssessment and treatment options
Bibliographic record
Abstract
Cognitive dysfunction is a recognized feature of mood disorders, including major depressive disorder (MDD) and bipolar disorder (BD). Cognitive impairment is associated with poor overall functional outcome and is therefore an important feature of illness to optimize for patients' occupational and academic outcomes. While generally people with BD appear to have a greater degree of cognitive impairment than those with MDD, direct comparisons of both patient groups within a single study are lacking. There are a number of methods for the assessment of cognitive function, but few are currently used in clinical practice. Current symptoms, past course of illness, clinical features, such as the presence of psychosis and comorbid conditions, may all influence cognitive function in mood disorders. Despite the general lack of assessment of cognitive function in clinical practice, clinicians are increasingly targeting cognitive symptoms as part of comprehensive treatment strategies. Novel pharmacological agents may improve cognitive function, but most studies of standard mood stabilizers, such as lithium and the anticonvulsants, have focused on whether or not the medications impair cognition. Non-pharmacological strategies, such as cognitive remediation and exercise, are increasingly studied in patients with mood disorders. Despite the growing interest in strategies to manage cognitive function, there is a paucity of high-quality trials examining either pharmacological or non-pharmacological modes of intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".