Cognitive dysfunction in major depression and bipolar disorder: <scp>A</scp>ssessment and treatment options
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it