Cognitive Function in Older Euthymic Bipolar Patients
Bibliographic record
Abstract
Objectives To assess cognitive function in older euthymic bipolar patients. To investigate the relationship between cognitive disorders and clinical features in this population. Methods We conducted a cross-sectional study during the period from August to November 2015. It included 34 stable bipolar outpatients, aged at least 65 years. We used the Montreal Cognitive Assessment (MoCA) to screen for cognitive disorders. Our patients were clinically euthymic, as checked by the Hamilton depression scale and the Young mania scale. Results The sex ratio was 1. The mean age of our patients was 68.2 years. Most of them were married (82.4%), unemployed (55.8%), living in urban area (82.4%), had low educational level (58.8%) and low income (64.7%). The majority was bipolar type 1 (67.6%). The most recent episode was manic in 55.9% of cases, including psychotic features in 50% of cases. Subsyndromal affective symptoms were noted between episodes in 23.5% of them. The average MoCA score was 23.6. Cognitive disorders were found in 61.5% of patients, who showed impairments across all cognitive domains. The most frequent deficits were found in attention (100%) and executive functions (85.3%). Cognitive dysfunction correlated to psychotic features during the last episode (P = 0.005), subsyndromal affective symptoms between episodes (P = 0.13), high number of mood episodes (P = 0.007) and hospitalisations (P = 0.014). Conclusion Our study confirmed that cognitive dysfunction was frequent in older bipolar patients in Tunisia. Preventing mood episodes, screening for addictive and somatic comorbidities, as well as cognitive rehabilitation, are suitable strategies for improving cognitive functioning among these patients. 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".