Quality assessment of systematic reviews of vitamin D, cognition and dementia
Bibliographic record
Abstract
BACKGROUND: There is conflicting evidence regarding the association of vitamin D with cognition performance and dementia.AimsWe aimed to summarise the evidence on the association of vitamin D with cognitive performance, dementia and Alzheimer disease through a qualitative assessment of available systematic reviews and meta-analyses. METHOD: We conducted an overview of the systematic reviews of all study types with or without meta-analyses on vitamin D and either Alzheimer disease, dementia or cognitive performance up to June 2017. RESULTS: Eleven systematic reviews were identified, nine of which were meta-analyses with substantial heterogeneity, differing statistical methods, variable methodological quality and quality of data abstraction. A Measurement Tool to Assess Systematic Reviews checklist scores ranged from 4 to 10 out of 11, with seven reviews of 'moderate' and four of 'high' methodological quality. Out of six meta-analyses on the association between low serum concentration of 25-hydroxyvitamin D and risk of dementia, five showed a positive association. Results of meta-analyses on the association between low serum concentration of 25-hydroxyvitamin D and memory function tests showed conflicting results. CONCLUSIONS: This systematic evaluation of available systematic reviews provided a clearer understanding of the potential link between low serum vitamin D concentrations and dementia. This evaluation also showed that the quality of the available evidence is not optimal because of both the low methodological quality of the reviews and low quality of the original studies. Interpretation of these systematic reviews should therefore be made with care.Declaration of interestNone.
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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 | 0.239 | 0.593 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.019 |
| Bibliometrics | 0.026 | 0.021 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".