Effect of alcohol intake on the development of mild cognitive impairment into dementia
Bibliographic record
Abstract
OBJECTIVE: To assess the dose-response relationship between alcohol consumption and the progression of MCI to dementia. METHOD: This study adheres to the Preferred Reporting Items for Systematic Reviews and Meta analysis for Protocols. Chinese Biomedical Literature Database (CBM), PubMed, Cochrane Library, EMBASE will be searched for all relevant published articles, with no restrictions on the year of publication or language. Case-control and cohort studies explored the relationship between alcohol exposure and the incidence of dementia in patients with MCI will be included. Study selection, data collection and assessment of study bias will be conducted independently at each level by a pair of independent reviewers. The Newcastle-Ottawa Scale (NOS) tool will be used for the risk of bias assessment. The methodological quality of systematic review will be based on A measurement Tool to Assess Systematic Review (AMSTAR 2). The Grading of Recommendations Assessment Development and Evaluation (GRADE) system will be used to assess the quality of evidence. Stata 15.0 will be used for general meta-analysis and exploring the dose-response relationship. Piecewise linear regression model and the restricted cubic spline model will be used for nonlinear trend estimation, and the generalized least-square method will be used to estimate the parameters. DISCUSSION: This dose-response meta-analysis is the first to investigate the dose-effect relationship between alcohol exposure and the incidence of dementia in patients with MCI, providing a comprehensive understanding of the prevention of alcohol-related cognitive impairment. REGISTRATION: The dose-response meta-analysis is registered in the PROSPERO (CRD42019127226) international prospective register of systematic review.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".