Assessment of cognitive impairment by using Addenbrooke’s Cognitive Examination (ACE) and Montreal Cognitive Assessment (MoCA) amongst type 2 diabetes mellitus patients in Eastern Uttar Pradesh, India
Bibliographic record
Abstract
Aim: The occurrence of diabetes mellitus (DM) is in increasing trend worldwide predominantly in South East Asian countries comprising India. Also, there is strong evidences that, DM upsurges the risk of cognitive impairment and dementia. Hence our main aim behind this study was to divulge the relationship between DM and mild cognitive impairment (MCI). Materials and Methods: A retrospective cohort study was conducted by reviewing and analyzing the medical records of DM who had been consulted at the neurology out-patient department during a period of 2015 - 2016. The cognitive impairment in DM patients were assessed by using Addenbrooke’s Cognitive Examination (ACE) and Montreal Cognitive Assessment (MoCA). Statistical Analysis: The data was analyzed using Statistical Package for Social Sciences (SPSS) version 19 software. If the p-value < 0> Results: The prevalence of cognitive impairment in DM patient is seemingly high with both scores i.e. 96.31% with MoCA and 99.85% with ACE-R. The variables like sex and age groups were not statistically significant with MoCA scores. The MoCA score & ACE-R score showed a negative association with education levels. The MoCA scores of different items i.e. visuo-excutive, naming, attention, language, abstraction and orientation were highly statistically significant at 0.01% level, between the DM patients who had cognitive impairment and DM patients with normal score, except memory which score zero in all patients. Conclusion: The prevalence of cognitive impairment in DM patients is very high. The, elder age, low education attainment, and unskilled occupation were also the potential identified risk factors for cognitive impairment. Keywords: Diabetes, Dementia, MoCA, Cognitive impairment, Low education etc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".