Depressive pseudodementia in Greek patients: How differential diagnosis can lead to early diagnosis
Bibliographic record
Abstract
Background The term Pseudodementia, as presented by Kiloh, is being used to describe the clinical image characterized by depression combined with impairment in cognitive functions which reacts positively in treatment with antidepressants. Aim To explore the aspects that make this condition unique, so that mental health professional will be able to use the proper psychometric tools when they face patients with confusing symptoms. Method Hundred and thirty-one participants were recruited from the B’ Psychiatric Clinic of G.H.N.P “Agios Panteleimon” and Day Center of Alzheimer's Disease in Amarousion, with 56 (42.7%) males and 75 (57.3%) females. All participants were administered the MoCA and the DASS21 questionnaires. Statistical analysis was performed with SPSS21. Results The findings reported a significant difference in the scores of MoCA done by patients with dementia (M = 13.9, SD = 5.4) and patients with depression (M = 20.5, SD = 4.9) while both groups scored below the accepted scores indicating cognitive impairment [CI]. However, analysis showed that in the following sectors of MoCA, depressive patients scored significantly higher than demented ones: visuospatial (MD = 0.651), clock (MD = 1.288), orientation (MD = 1.212) and delayed recall (MD = 1.329). Conclusion Findings shows a significant pattern in the difference between depressed and patients with cognitive impairment. These findings suggest that mental health professionals should use neuropsychological measurements like MoCA when evaluating such cases in order to be able to diagnose effectively cases of pseudodementia. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".