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Record W4205299597 · doi:10.1002/alz.12544

Is more always better? Dose effect in a multidomain intervention in older adults at risk of dementia

2022· article· en· W4205299597 on OpenAlexafffund
Sylvie Belleville, Simon Cloutier, Samira Mellah, Sherry L. Willis, Bruno Vellas, Sandrine Andrieu, Nicola Coley, Tiia Ngandu

Bibliographic record

VenueAlzheimer s & Dementia · 2022
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversité de MontréalInstitut Universitaire de Gériatrie de Montréal
FundersCanadian Institutes of Health Research
KeywordsDementiaIntervention (counseling)GerontologyMedicinePsychologyPsychiatryInternal medicineDisease

Abstract

fetched live from OpenAlex

BACKGROUND: Little is known regarding the dose-response function in multidomain interventions for dementia prevention. METHOD: The Multidomain Alzheimer Preventive Trial is a 3-year randomized controlled trial comprising cognitive training, physical activity, nutrition, and omega-3 polyunsaturated fatty acids for at-risk older adults. The dose delivered (number of sessions attended) was modeled against global cognition, memory, and fluency in 749 participants. Interaction effects were assessed for age, sex, education, dementia score (CAIDE), frailty score, and apolipoprotein E (APOE) ε4 status. RESULTS: The dose-response models were non-linear functions indicating benefits up to about 12 to 14 training hours or 15 to 20 multidomain sessions followed by a plateau. Participants who benefited from a higher dose included women, younger participants, frail individuals, and those with lower education or lower risk of dementia. DISCUSSION: The non-linear function indicates that a higher dose is not necessarily better in multidomain interventions. The optimal dose was about half of the potentially available sessions.

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)0.007
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.013
GPT teacher head0.304
Teacher spread0.291 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
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

Citations44
Published2022
Admission routes2
Has abstractyes

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