Association between alcohol consumption and mild cognitive impairment
Bibliographic record
Abstract
OBJECTIVE: The objective of this study is to investigate the potential dose-response association between alcohol consumption and the risk of mild cognitive impairment (MCI). METHODS: We will perform a dose-response meta-analysis (DRMA) of cohort studies to explore the dose-response relationship between alcohol intake and MCI. A comprehensive literature search of PubMed, EMBASE, The Cochrane Library, Chinese BioMedical Literature Database (CBM), China National Knowledge Infrastructure (CNKI), VIP, and Wan-Fang Database will be conducted. Two investigators will independently select studies, extract data, and assess the quality of the included study. The Newcastle-Ottawa Scale will be used to assess the quality of include studies. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system and A MeaSurement Tool to Assess systematic Reviews (AMSTAR) will be used to assess the quality of evidence and methodological quality. Any disagreement will be resolved by the third investigator. We will use the hazard ratio as the effect indicator, and piecewise linear regression model and restricted cubic spline model will be used for linear and nonlinear trend estimation, respectively. There is no requirement of ethical approval and informed consent. DISCUSSION: This is the first DRMA to explore the dose-response relationship between alcohol intake and MCI. We predict it will provide high-quality evidence to prevent clinical MCI and dementia. REGISTRATION: The DRMA is registered in the PROSPERO (CRD42019127261) international prospective register of systematic review.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".