Dementia among older patients attending National Institute of Mental Health (NIMH), Dhaka, Bangladesh
Bibliographic record
Abstract
Dementia has become the focus of attention of health care professionals worldwide. The objective of the study was to find out the proportion of dementia among older patients as well as to identify the socio-demographic characteristics of patients with dementia attending National Institute of Mental Health (NIMH), Sher-E-Bangla Nagar, Dhaka, Bangladesh. This was a cross sectional study conducted in NIMH during the period from 1st November 2014 to 30th April 2015. For this purpose, 78 elderly patients aged e60 years attending both in outpatient and inpatient departments of NIMH, satisfying inclusion and exclusion criteria were selected by convenient sampling technique. Data were collected by face-to-face interview using semi-structured questionnaire. Cognitive impairment was assessed by Bengali version of Mini Mental State Examination (MMSE) and dementia was diagnosed according to Diagnostic and Statistical Manual for Mental Disorders, 5th edition (DSM-5). The results showed that most of the patients (51.3%) were in between the ages of 60 to 64 years with male predominance (56.4%). The mean (± SD) age of the patients was 66.84 (±5.49) years. Among the patients 43.6 % came from urban area, 39.7% from the family with monthly income within 30001-45000 Bangladeshi taka (BDT) and 53.9% had family members in between 4 to 6. Among them 61.5% patients were married, 43.6% were retired from service, 32.1% studied up to primary level and 62.8% had caregivers. Most of them (88.5%) scored between 24-30 in Mini Mental State Examination (MMSE) and only 5.1% respondents had dementia. This study has provided baseline information about the proportion of dementia among elderly patients in Bangladesh that can be used in future studies.Bang J Psychiatry June 2015; 29(1): 5-9
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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 | 0.001 | 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".