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Record W4367053085 · doi:10.3389/fnagi.2023.1051809

Gout and risk of dementia, Alzheimer's disease or vascular dementia: a meta-epidemiology study

2023· review· en· W4367053085 on OpenAlex
Xuanlin Li, Lin Huang, Yujun Tang, Xuanming Hu, Chengping Wen

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2023
Typereview
Languageen
FieldMedicine
TopicGout, Hyperuricemia, Uric Acid
Canadian institutionsnot available
FundersNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsMedicineDementiaInternal medicineVascular dementiaMeta-analysisPublication biasRelative riskGoutCohort studyConfidence intervalDisease

Abstract

fetched live from OpenAlex

Objectives The association between gout and dementia, Alzheimer's disease (AD), or vascular dementia (VD) is not fully understood. The aim of this meta-analysis was to evaluate the risk of all-cause dementia, AD, and VD in gout patients with or without medication. Methods Data sources were PubMed, Embase, the Cochrane Library, and reference lists of included studies. This meta-analysis included cohort studies assessing whether the risk of all-cause dementia, AD, and VD was associated with gout. The risk of bias was assessed using the Newcastle-Ottawa Quality Assessment Scale (NOS). The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system was used to access the overall certainty of evidence. Risk ratios ( RR ) with 95% confidence intervals ( CI ) were pooled using a random-effects model, and publication bias was assessed with funnel plots and Egger's test. Results A total of six cohort studies involving 2,349,605 individuals were included in this meta-analysis, which were published between 2015 and 2022. The pooling analysis shows that the risk of all-cause dementia was decreased in gout patients [ RR = 0.67, 95% CI (0.51, 0.89), I 2 = 99%, P = 0.005, very low quality], especially in gout patients with medication [ RR = 0.50, 95% CI (0.31, 0.79), I 2 = 93%, P = 0.003, low quality]. The risk of AD [ RR = 0.70, 95% CI (0.63, 0.79), I 2 = 57.2%, P = 0.000, very low quality] and VD [ RR = 0.68, 95% CI (0.49, 0.95), I 2 = 91.2%, P = 0.025, very low quality] was also decreased in gout patients. Despite the large heterogeneity, the sensitivity analysis indicated that the results were robust, and there was little evidence of publication bias. Conclusion The risk of all-cause dementia, AD, and VD is decreased in gout patients, but the quality of evidence is generally low. More studies are still needed to validate and explore the mechanisms of this association. Systematic review registration https://www.crd.york.ac.uk/prospero/#recordDetails , identifier: CRD42022353312.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMeta-epidemiology (broad)
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Meta-analysishigh
gptMeta-epidemiology (broad)
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Meta-analysishigh
models agreeAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.616
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.168
GPT teacher head0.385
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it