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Record W3102862585 · doi:10.1002/trc2.12083

CCCDTD5: Reducing the risk of later‐life dementia. Evidence informing the Fifth Canadian Consensus Conference on the Diagnosis and Treatment of Dementia (CCCDTD‐5)

2020· article· en· W3102862585 on OpenAlexaffabout
Kenneth Rockwood, Melissa K. Andrew, Mylène Aubertin‐Leheudre, Sylvie Belleville, Louis Bherer, Susan K. Bowles, D. Scott Kehler, Andrew Lim, Laura E. Middleton, Natalie A. Phillips, Lindsay Wallace

Bibliographic record

VenueAlzheimer s & Dementia Translational Research & Clinical Interventions · 2020
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsConcordia UniversityHealth Sciences CentreUniversité de MontréalUniversity of WaterlooSunnybrook Health Science CentreInstitut Universitaire de Gériatrie de MontréalUniversité du Québec à MontréalUniversity of TorontoDalhousie University
Fundersnot available
KeywordsDementiaCognitive declineCognitionPsychologyConsensus conferenceMedicineGerontologyPsychiatryDisease

Abstract

fetched live from OpenAlex

The Fifth Canadian Consensus Conference on the Diagnosis and Treatment of Dementia (CCCDTD-5) was a year-long process to synthesize the best available evidence on several topics. Our group undertook evaluation of risk reduction, in eight domains: nutrition; physical activity; hearing; sleep; cognitive training and stimulation; social engagement and education; frailty; and medications. Here we describe the rationale for the undertaking and summarize the background evidence-this is also tabulated in the Appendix. We further comment specifically on the relationship between age and dementia, and offer some suggestions for how reducing the risk of dementia in the seventh decade and beyond might be considered if we are to improve prospects for prevention in the near term. We draw to attention that a well-specified model of success in dementia prevention need not equate to the elimination of cognitive impairment in late life.

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 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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.533
GPT teacher head0.504
Teacher spread0.029 · 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 teacher head, not a consensus.

Study designObservational
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

Citations13
Published2020
Admission routes2
Has abstractyes

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