Assessing and validating reliable change across ADNI protocols
Bibliographic record
Abstract
OBJECTIVE: Reliable 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. METHOD: = 192 each) of robustly cognitively intact community-dwelling older adults from ADNI - matched for demographic and testing factors. The developed formulae for each sample were then applied to one of the samples to determine goodness-of-fit and appropriateness of combining samples for a single set of SRB equations. RESULTS: Minimal 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. CONCLUSIONS: Establishing 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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".