EPA-1162 – Underrecognition of dementia in nursing home residents in tunisia
Bibliographic record
Abstract
Dementia is one of the most common neuropsychiatric disorders in nursing home residents. Despite a high prevalence and a major impact on daily activities, dementia remains largely underdiagnosed and therefore rarely treated in nursing homes throughout the world. This study aims to determine the prevalence of dementia among residents of Manouba nursing home (in Tunis) and to identify the proportion of recognised cases of dementia among this population. A cross-sectional study was performed from September to October 2012 among all consenting residents of Manouba nursing home. Sociodemographic data were collected by the means of a semi-structured questionnaire. Cognitive functions were assessed using the Montreal Cognitive Assessment (MoCA) test in its Arabic version. We excluded subjects who could not take the MoCA test because of a severe sensory deficit or a severe psychiatric condition. At the time of the study, the population of Manouba nursing home residents consisted of 116 subjects. After applying the exclusion criteria, we retained 77 subjects: 48 males (62.3%) and 29 females (37.7%). The average age was 72.6 years. The average MoCA score was 14.4 +/− 6.5 (range: 3 to 29). The prevalence of dementia in our population was 58.4% (n=45). Among these 45 subjects with dementia, only three (or 6.7% of dementia cases) were already diagnosed as such. Of these three patients, only one was on anticholinesterase treatment. Dementia is largely underrecognised and hence undertreated in nursing homes in Tunisia as in other countries. Routine screening for cognitive deficits seems paramount in order to ensure early diagnosis and treatment of this debilitating condition.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".