Clinical utility of cognitive screening instruments in dementia patients of neurology OPD (Memory Clinic) in Eastern Uttar Pradesh, India
Bibliographic record
Abstract
Introduction: Dementia is a major public health challenge that is becoming more common with growing age. It involves progressive and often remorseless decline in cognition, functions, behavior and care needs. The Assessment of dementia greatly relies on collateral as well as patient-derived information. The use of various cognitive tests assumes great importance during the process of diagnostic investigation. In this research we attempt to assess the practical usage of various cognitive scales. Materials and Methods: In this study, we studied the data of 233 patients in the neurology OPD. The following cognitive scales like Montreal Cognitive Assessment (MoCA), Addenbrooke’s Cognitive Examination (ACE), Hachinski Ischemic Score (HIS), and Everyday Abilities Scale for India (EASI) were performed on the above selected data of patient. Results: 228 (97.85) % of patients were found to have cognitive impairment when screened with MoCA. However, 230 (98.7%) of patients screened with EASI had a disability score. The patients with a HIS score of more than 7 was (64) 27.9%. Whereas, ACE indicated all patients had score below 83 (100%). The sensitivity of MoCA, ACE, and EASI score was 72.69% and 72.29%, 71.74% respectively with a specificity of 50 and 100, 0.0% respectively for the above scores, whereas the sensitivity and specificity of HIS was 49.50% and 10.61. Conclusion: MoCA, ACE, and EASI tools have good capability to detect most patients with cognitive impairments. They are sensitive to predict probable cases of dementia, therefore can be used as an efficient diagnostic tool in quick screening of cognitive impairment. Keywords: Montreal Cognitive Assessment (MoCA), Addenbrooke’s Cognitive Examination (ACE), Hachinski Ischemic Score (HIS), Everyday Abilities Scale for India (EASI), and 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".