Alcohol consumption and risk of dementia
Bibliographic record
Abstract
OBJECTIVE: To investigate the relationship between alcohol consumption and the incidence of dementia. METHOD: We will conduct a systematic search without language and year restrictions to identify all relevant published studies. The following electronic databases will be searched: PubMed, EMBASE, the Cochrane Library, Chinese BioMedical Literature Database (CBM) and China National Knowledge Infrastructure (CNKI), VIP, Wan-Fang. Cohort studies published in Chinese or English are considered for inclusion. Two authors will independently select studies base on inclusion criteria, extract data and assess the quality of included studies using the Newcastle-Ottawa Scale, the Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) system will be used to quantify absolute effects and quality of evidence. Any disagreement will be resolved by consensus. We will use the hazard ratio (HR) as the effect indicator, piecewise linear regression model and restricted cubic spline model will be used for linear and nonlinear trend estimation, respectively. REGISTRATION: The dose-response meta-analysis is registered in the PROSPERO (CRD42019127367) international prospective register of systematic review. DISCUSSION: In the previous related dose-response meta-analysis studies, there were some limitations: on the 1 hand, the sex was not taken into account. On the other hand, relative risk (RR) is not the best effect indicator for time-to-event data, but compare with RR, HR is much better. This study intends to use HR as the effect indicator to explore the dose-response relationship and the sex difference between alcohol intake and dementia. Accurate alcohol drinking data can provide high-quality evidence for the prevention of dementia.
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 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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".