The Prevalence of Comorbidities and Associated Factors among Patients with Dementia in the Indian Setting: Meta-analysis of Observational Studies
Bibliographic record
Abstract
Background: Patients with dementia usually have multiple comorbidities. The presence of comorbidities may exacerbate the progression of dementia and decreases the patient's ability to participate in health maintenance activities. However, there is hardly any meta-analysis estimating the magnitude of comorbidities among patients with dementia in the Indian context. Methods: statistics were calculated to measure heterogeneity among studies. Results: Fourteen studies were included in the meta-analysis based on the inclusion and exclusion criteria. Altogether, we found the coexistence of comorbid conditions such as hypertension (51.10%), diabetes (27.58%), stroke (15.99%), and factors like tobacco use (26.81 %) and alcohol use (9.19%) among patients with dementia in this setting. The level of heterogeneity was high due to differences in the methodologies in the included studies. Conclusions: Our study found hypertension as the most common comorbid condition among patients with dementia in India. The observed lacuna of methodological limitations in the studies included in the current meta-analysis provides the urgent need for good quality research to successfully meet the challenges ahead while devising appropriate strategies to treat the comorbidities among patients with dementia.
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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 | 0.019 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.045 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".