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Record W4406224831 · doi:10.1002/alz.085067

Triangulation to make decisions about what are modifiable risk factors and the new risk factors

2024· article· en· W4406224831 on OpenAlexaff
Naaheed Mukadam, Gill Livingston, Jonathan Huntley, Kathy Liu, Sergi Costafreda Gonzalez, Geir Selbæk, Suvarna Alladi, David Ames, Sube Banerjee, Alistair Burns, Carol Brayne, Nick C. Fox, Cleusa P. Ferri, Laura N. Gitlin, Robert Howard, Helen C. Kales, Mika Kivimäki, Eric B. Larson, Noeline Nakasujja, Kenneth Rockwood, Quincy M. Samus, Kokoro Shirai, Archana Singh‐Manoux, Lon S. Schneider, Sebastian Walsh, Yao Yao, Andrew Sommerlad

Bibliographic record

VenueAlzheimer s & Dementia · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTriangulationRisk analysis (engineering)PsychologyComputer scienceMedicineGeographyCartography

Abstract

fetched live from OpenAlex

Abstract Background The 2020 Lancet Commission on dementia prevention, intervention and care estimated that up to 40% of dementia cases could be prevented by tackling 12 potentially modifiable risk factors, namely less education, hearing loss, hypertension, physical inactivity, diabetes, social isolation, excessive alcohol consumption, air pollution, smoking, obesity, traumatic brain injury, depression. As more evidence on risk factors emerges, the Lancet standing commission on dementia met to update evidence on established dementia risk factors and to consider the evidence for other risk factors. Method We used a lifecourse approach to understand how to reduce risk or prevent dementia, as many risks operate at different timepoints in the lifespan. We considered evidence for when in the lifecourse a risk factor was relevant to development of dementia as well as the size of the effect and strength of the evidence. Our interdisciplinary, international, multicultural group of experts adopted a triangulation framework, prioritising systematic reviews and meta‐analyses, performing new meta‐analyses where needed and debated and agreed on the best available evidence and its consistency. We considered whether there was evidence of disparities in impact of risk factors based on demographic characteristics, particularly ethnicity and socioeconomic status. Result Evidence for two new risk factors was considered strong enough to include this in our lifecourse model. We will present evidence for incorporation of these risk factors, including strength of evidence and potential mechanisms. We will also discuss risk factors for which evidence was not strong enough. Conclusion As more evidence about risk factors emerges we can increase our understanding of how dementia develops and how to potentially prevent it. Understanding the landscape of dementia prevention research is also helpful to appreciate where further evidence is needed and what form of evidence would be most helpful in advancing our understanding.

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 imitation

Not 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.

metaresearch head score (Codex)0.423
metaresearch head score (Gemma)0.697
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.423
Threshold uncertainty score0.711

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4230.697
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0120.014
Bibliometrics0.0230.014
Science and technology studies0.0050.010
Scholarly communication0.0170.022
Open science0.0100.016
Research integrity0.0100.012
Insufficient payload (model declined to judge)0.0170.004

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 head0.049
GPT teacher head0.304
Teacher spread0.255 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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