Risk factors for predicting progression from normal cognition to mild cognitive impairment: protocol for a systematic review and meta-analysis of cohort studies
Bibliographic record
Abstract
INTRODUCTION: Mild cognitive impairment (MCI) often represents the earliest stage of Alzheimer's disease. There has been considerable research investigating specific risk factors regarding the progression from normal cognition to MCI. However, different studies have come to different conclusions on the impact of particular risk factors. Therefore, it is necessary to conduct a meta-analysis of the risk factors that predict cognitive disruption in individuals based on associations with MCI. METHODS AND ANALYSIS: We will search seven electronic databases without time limit, including MEDLINE, EMBASE, The Cochrane Library, Web of Science, China National Knowledge Infrastructure, the Wan Fang Database and China Biology Medicine. Two researchers will independently screen for eligibility and perform data extraction. Data were extracted from cohort studies meeting the inclusive criteria according to the Newcastle Ottawa Scale (NOS) methods. A third member of the research team will be contacted when a consensus cannot be reached. Any disagreement will be settled by consensus. The NOS will be used to assess the quality of the studies. All analyses were performed using Stata V.15.1. ETHICS AND DISSEMINATION: We will report this review in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis statement. We will disseminate our findings through a publication in a peer-reviewed journal. This systematic review does not require ethical approval as no primary data are collected. PROSPERO REGISTRATION NUMBER: CRD42018109099
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 | 0.106 | 0.119 |
| Meta-epidemiology (narrow) | 0.008 | 0.006 |
| Meta-epidemiology (broad) | 0.021 | 0.031 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.043 | 0.006 |
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".