Prevalence of dementia in Egypt: a systematic review
Bibliographic record
Abstract
BACKGROUND: With the growing prevalence of dementia worldwide, two-third of the people with dementia are projected to be from the developing countries by 2050. AIM: This study reviews the literature regarding dementia prevalence in Egypt. METHODS: Six databases were systematically searched from their dates of inception till July 2016. Studies published in English and reporting dementia prevalence among nonhospitalized individuals after clinical examinations were considered eligible. References were screened independently by two reviewers in two steps: 1) abstract screening and 2) full-text reviewing. In addition, quality of the included studies was assessed using the Newcastle-Ottawa scale. RESULTS: Of the 1,630 references retrieved, six studies (n=28,029 participants) met our inclusion criteria. In all studies, dementia was ascertained using a three-phase survey (Phase I: screening, Phase II: clinical diagnosis, Phase III: laboratory investigations). The dementia prevalence ranged from 2.01% to 5.07%. Dementia increased with age, with the rapid increase among those aging ≥80. Also, its prevalence was higher among illiterate groups than among educated groups. Included studies were of low risk of bias. CONCLUSION: Dementia prevalence in Egypt demands including people with dementia in the health care system and promoting the awareness of dementia among the public. Also, more epidemiological studies in this field are needed.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".