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Record W4327794801 · doi:10.14283/jpad.2023.29

Combined Evidence for a Long-Term, Clinical Slowing Effect of Multinutrient Intervention in Prodromal Alzheimer's Disease: Post-Hoc Analysis of 3-Year Data from the LipiDiDiet Trial

2023· article· en· W4327794801 on OpenAlexaff
S B Hendrix, H. Soininen, A. Solomon, Pieter Jelle Visser, Anneke M.J. van Hees, D S Counotte, J Nicodemus-Johnson, S P Dickson, K. Blennow, M. Kivipelto, Tobias Hartmann

Bibliographic record

VenueThe Journal of Prevention of Alzheimer s Disease · 2023
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsHendrix Genetics (Canada)
FundersEuropean CommissionDanone Nutricia ResearchKuopion Yliopistollinen SairaalaBundesministerium für Bildung und ForschungDanone
KeywordsPost-hoc analysisMedicineClinical trialPost hocRandomized controlled trialDiseaseIntervention (counseling)Internal medicinePsychiatry

Abstract

fetched live from OpenAlex

The LipiDiDiet randomized clinical trial is evaluating the long term effects of a multinutrient intervention (Fortasyn Connect) compared with control in participants with prodromal AD. In this post-hoc analysis we used the Alzheimer's Disease Composite Score (ADCOMS) as a measure of cognition and global function, together with a global statistical test (GST) and Bayesian hierarchical modelling (BHM) to evaluate the totality of evidence for an effect of the intervention over 36 months. The analysis includes 67 participants (39 active, 28 control) with change from baseline data after 36 months intervention. All outcome measures showed a statistically significant effect for the intervention: ADCOMS (P =0.045), GST (P <0.001), and BHM (P =0.008 based on 3 outcomes and P <0.001 including all primary and secondary quantitative clinical outcomes). Fortasyn Connect was associated with significantly less clinical decline over 36 months, suggesting the long-lasting beneficial effects of the multinutrient in prodromal AD.

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)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.442
Threshold uncertainty score0.641

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.177
GPT teacher head0.470
Teacher spread0.293 · 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.

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

Citations6
Published2023
Admission routes1
Has abstractyes

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