Major Depressive Disorder as a risk factor of neuropsychiatric symptoms in normal and pathological aging, and associations with cognitive performances
Bibliographic record
Abstract
Abstract Objectives The diagnosis of Major Depressive Disorder (MDD) is based on the DSM-V criteria and is established by a clinician. It allows quantifying depression based on clinical criteria. As such, MDD differs from other types of depressions quantified based on subjective scales. Here, we evaluated the MDD risk factor on other neuropsychiatric symptoms (NPS) as well as MDD association with cognitive performance in Alzheimer’s disease (AD), Mild Cognitive Impairment (MCI) and Healthy Controls (CH). Participants Data of 208 patients with AD, 291 patients with MCI and 647 HC was extracted from the National Alzheimer’s Coordinating Center database. Each included participant was assessed by a physician for the MDD criteria, underwent an evaluation of NPS using the NeuroPsychiatric Inventory, and a comprehensive cognitive assessment. Participants were classified in those with- and without MDD. We performed logistic regression and a MANCOVA models respectively with NPS and cognitive performance as variables of interest and MDD as fixed factors within each group. The MANCOVA was controlled for the effects of age, sex, and education. Results MDD increased the risk for psychotic, affective and behavioral NPS in MCI, and affective/behavioral NPS in CH and AD. Also, MCI with MDD had lower performance on selective attention and mental flexibility. Conclusions MDD seems to increase the probability for a higher prevalence of NPS in all groups (CN, MCI and AD). This might suggest that early treatment of MDD could impact future neuropsychiatric symptomatology and cognitive performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".