Validity and performance of cognitive scales in elderly patients with depression in a tertiary care hospital in Chennai
Bibliographic record
Abstract
Background: Late-life depression in older adults can cause reversible cognitive impairment, often resulting in pseudo-dementia. Cognitive impairment can lead to executive dysfunction, reduced flexibility, and difficulty thinking and decision-making. This study aimed to assess the validity and performance of cognitive scales in late-life depression among patients attending the outpatient department of the institute of mental health, Chennai. Methods: This prospective study included 360 patients aged >50 years who were diagnosed with depression and attended the OPD at the institute of mental health, Chennai. Baseline assessments were performed at the time of recruitment into the study (visit 1), and scheduled visits were performed every six months for two years (visits 2 to 5). Unscheduled visits were done every month, and adverse events were monitored and recorded periodically Results: Among 59 patients, 53.1% were female, 32.2% were diabetic, and 93.9% were experiencing subjective working difficulties. The Montreal cognitive assessment scale classified 51.4% as moderate, while the ADAS-cog and ACE scales classified 86% and 99.7%, respectively, as having abnormal mental status. However, a significant correlation and discrepancy between scores were observed for scales such as ACE, ADAS-cog, standardised mini-mental status examination, and Montreal cognitive assessment scale. A strong correlation was found between ACE, MMSE, MoCA, and ACE; however, FAST showed a significant negative correlation. The MoCA was strongly correlated with the MMSE, ACE, ADAS-cog, and Mini-Cog, indicating good alignment with the FAST. Conclusions: Cognitive scales strongly correlate with late-life depression in patients, suggesting an improvement in assessment, evaluation, and treatment to address cognitive deficits.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".