Incidence and prevalence of Lewy body dementia in India: A systematic review
Bibliographic record
Abstract
With increasing life expectancy in India, the prevalence of age-related disorders, such as dementia has also increased. Health and social care resources for each state are allocated based on their inhabitants’ age, sex, education, and urban/rural status but not on the dementia subtype, which can significantly influence prognosis, healthcare utilization, and quality of life. Herein, we aimed to systematically review studies investigating the prevalence of the Lewy body dementia (LBD) subtype in India. We conducted a systematic review of EMBASE, MEDLINE, and APA PsychINFO databases on June 22, 2023. Two independent reviewers performed screening and full-text review, with a third reviewer resolving any disputes. Quality was assessed for each extracted paper. Of 1372 identified studies, full-text reviews were conducted on 399 and data were extracted from 4. Two studies included prevalence data on dementia with Lewy bodies (DLB), one on Parkinson’s disease dementia and one on LBD. DLB or LBD has been reported to represent 1.0 – 8.9% of dementia diagnoses. Methodological heterogeneity was characterized by study design, access to biomarkers, diagnostic criteria, and use of cognitive tools. No studies reported incidence data. A paucity of research on LBD epidemiology in India is compounded by methodological heterogeneity, poorly representative cohorts, and varying access to biomarkers. Consensus guidelines may support data harmonization and the creation of multisite consortia, which could redress the under-representation of Central Asian data in epidemiological and genetic LBD studies.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".