ArticleJournal of clinical and experimental neuropsychology2022
Assessing and validating reliable change across ADNI protocols.
Article in Journal of clinical and experimental neuropsychology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Cognitive benefits of a lifestyle-induced prediabetes remission intervention in Canada.Preventive medicine reports · 2026Article
- Cognitive dispersion profiles and prediction of cognitive change in early-onset dementias: Results from LEADS.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Slow-4 cerebellar activity tracks MMSE language-related task performance across the Alzheimer's disease spectrum: a multi-metric resting-state fMRI study.Frontiers in neuroscience · 2026Article
- Characterizing and validating 12-month reliable cognitive change in Early-Onset Alzheimer's Disease for use in clinical trials.The journal of prevention of Alzheimer's disease · 2025Article
- Axonal injury, sleep disturbances, and memory following traumatic brain injury.Annals of clinical and translational neurology · 2024Article
- Machine learning on longitudinal multi-modal data enables the understanding and prognosis of Alzheimer's disease progression.iScience · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
objectiveReliable change methods can aid in determining whether changes in cognitive performance over time are meaningful. The current study sought to develop and cross-validate 12-month standardized regression-based (SRB) equations for the neuropsychological measures commonly administered in the Alzheimer's Disease Neuroimaging Initiative (ADNI) longitudinal study.
methodPrediction algorithms were developed using baseline score, retest interval, the presence/absence of a 6-month evaluation, age, education, sex, and ethnicity in two different samples (
resultsMinimal differences were seen between Observed 12-month and Predicted 12-month scores on most neuropsychological tests from ADNI, and when compared across samples the resultant Predicted 12-month scores were highly correlated. As a result, samples were combined and SRB prediction equations were successfully developed for each of the measures.
conclusionsEstablishing cross-validation for these SRB prediction equations provides initial support of their use to detect meaningful change in the ADNI sample, and provides the basis for future research with clinical samples to evaluate potential clinical utility. While some caution should be considered for measuring true cognitive change over time - particularly in clinical samples - when using these prediction equations given the relatively lower coefficients of stability observed, use of these SRBs reflects an improvement over current practice in ADNI.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.