Major Depressive Disorder with Psychotic Features May Lead to Misdiagnosis of Dementia
Bibliographic record
Abstract
Major depressive disorder (MDD) with psychotic features is relatively frequent in patients with greater depressive symptom severity and is associated with a poorer course of illness and greater functional impairment than MDD without psychotic features. Multiple studies have found that patients with psychotic mood disorders demonstrate significantly poorer cognitive performance in a variety of areas than those with nonpsychotic mood disorders. The Mini Mental State Examination (MMSE) and the Dementia Rating Scale, Second Edition (DRS-2) are widely used to measure cognitive functions in research on MDD with psychotic features. Established total raw score cut-offs of 24 on the MMSE and 137 on the DRS-2 in published manuals suggest possible global cognitive impairment and dementia, respectively. Limited research is available on these suggested cut-offs for patients with MDD with psychotic features. We document the therapeutic benefit of electroconvulsive therapy (ECT), which is usually associated with short-term cognitive impairment, in a 68-year-old woman with psychotic depression whose MMSE and DRS-2 scores initially suggested possible global cognitive impairment and dementia. Over the course of four ECT treatments, the patient's MMSE scores progressively increased. After the second ECT treatment, the patient no longer met criteria for global cognitive impairment. With each treatment, depression severity, measured by the 24-item Hamilton Rating Scale for Depression, improved sequentially. Thus, the suggested cut-off scores for the MMSE and the DRS-2 in patients with MDD with psychotic features may in some cases produce false-positive indications of dementia.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".