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Record W4390192389 · doi:10.1002/alz.076315

The PACt‐MD randomized clinical trial: Prevention of Alzheimer’s dementia with Cognitive remediation plus transcranial direct current stimulation in Mild cognitive impairment and Depression

2023· article· en· W4390192389 on OpenAlexaff
Tarek K. Rajji, Christopher R. Bowie, Nathan Herrmann, Bruce G. Pollock, Krista L. Lanctôt, Sanjeev Kumar, Alastair J. Flint, Linda Mah, Corinne E. Fischer, Meryl A. Butters, Marom Bikson, Daniel M. Blumberger, Zafiris J. Daskalakis, Mark Rapoport, Nicolaas Paul L.G. Verhoeff, Angela Golas, Ariel Graff‐Guerrero, Érica Leandro Marciano Vieira, Aristotle N. Voineskos, Heather Brooks, Ashley Melichercik, Kevin E. Thorpe, Benoit H. Mulsant

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsSt. Michael's HospitalBaycrest HospitalHealth Sciences CentreQueen's UniversityUniversity Health NetworkToronto Dementia Research AllianceUniversity of TorontoSunnybrook Health Science CentreCentre for Addiction and Mental Health
Fundersnot available
KeywordsTranscranial direct-current stimulationRandomized controlled trialDementiaMedicineCognitive declineCognitionEffects of sleep deprivation on cognitive performancePhysical therapyPsychologyInternal medicinePsychiatryStimulation

Abstract

fetched live from OpenAlex

Abstract Background Interventions to prevent cognitive decline and dementia in high‐risk populations such as those with remitted Major Depressive Disorder (rMDD) or Mild Cognitive Impairment (MCI) are urgently needed. Method PACt‐MD was a double‐blind randomized trial conducted between 2015 and 2022 comparing cognitive remediation (CR) plus transcranial Direct Current Stimulation (tDCS) vs. sham‐CR+sham‐tDCS delivered 5 days/week for 8 weeks followed by 5‐day semi‐annual boosters and at‐home daily CR, in participants with rMDD or MCI. Participants were assessed at baseline, week‐8, and yearly. The hypotheses were that compared to sham+sham, CR+tDCS would: slow cognitive decline (H1); reduce progression to MCI or dementia (H2); and acutely improve cognition (H3). The primary outcome composite score was calculated when at least half of the tests for at least four of six cognitive domains were completed. Due to COVID‐19, some participants did not complete enough tests and a second composite score using all available data was generated. Result 375 participants were randomized (Active: N = 188, Mean Age = 72.1 ± 6.3; Sham: N = 187, Mean Age = 72.3 ± 6.4) and received at least one intervention session. Over up to six years, there was no time‐by‐treatment interaction using the primary composite score but there was using the all‐data score. Change in composite score differed between the two intervention groups for the primary and all‐data scores for all years except for year‐6 primary score. The year‐5 adjusted z‐score difference was ‐0.15 (95%CI [‐0.29, ‐0.005]) for the primary and ‐0.21 (95%CI [‐0.34, ‐0.067]) for all‐data score (Fig.1). Comparing active vs. sham at 8‐week (H3), the adjusted z‐score difference was ‐0.06 (95%CI: [‐0.12, 0.005]; p = 0.072); and for progression (H2), HR was 0.66 (95%CI [0.40, 1.08] p = 0.101) in a stratified Cox model. In a preplanned secondary analysis of domain scores, there was a time‐by‐treatment interaction for verbal memory with a year‐5 adjusted z‐score difference of ‐0.30 (95%CI [‐0.53, ‐0.074]) (Fig.2) but not the other domains. In another preplanned secondary analysis, the model with a randomization diagnosis‐by‐time‐by‐treatment was different from the model without this three‐way interaction (p = 0.012; Fig.3). Conclusion CR+tDCS may be effective in slowing cognitive decline (particularly verbal memory) in older patients with rMDD or MCI.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.105
GPT teacher head0.381
Teacher spread0.276 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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