How much can the Alzheimer’s Disease Assessment Scale Cognitive Subscale (ADAS‐Cog) tell us? Insights from a latent state‐trait auto‐regressive (LST‐AR) model
Bibliographic record
Abstract
Abstract Background The ADAS‐Cog assesses several cognitive domains and is the international gold standard for monitoring cognitive decline in clinical trials for Alzheimer’s disease (AD) dementia. These manifold cognitive deficits can progress differently and therefore, individual ADAS‐Cog items may contain different sources of information: some predictable based on the baseline score (i.e. trait), and some based on progression from one occasion to the next (i.e. accumulated or “autoregressive” effects), while some reflects unpredictable “occasion specific” fluctuations in symptoms (i.e state). Method Over multiple occasions of measurement, latent state‐trait autoregressive (LST‐AR) models can be used to decompose each item into reliable consistent information (composed of trait information and accumulated effects), reliable occasion‐specific information (i.e. state effects), and unreliable information (i.e. measurement error). Thus, consistency (trait + accumulated effects) and reliability (consistency + occasion specificity) can be determined for each item, at each assessment. The LST‐AR model was fit to four waves of data assessments (baseline, 6, 12 and 24 months) among 341 mild AD patients that participated in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. Result The reliabilities of the memory (43.4%‐80.1%) and language (63.8%‐92.5%) items were generally greater than those of the praxis (average of 65.6%) and orientation (average of 60.8%) items. At the last assessment, all of the memory and language items had more consistency (ranging from 63.4% for comprehension of spoken language to 88.2% for word recognition) than occasion specificity. Disentangling the consistency into traits and accumulated effects, the items most reflective of traits were word recognition (79.4%) and commands (64.1%). Word recall, orientation and naming tasks contained roughly equal amounts of trait and accumulated information. Scores on comprehension of spoken language and spoken language ability were mainly related to accumulated effects (71.4% and 64.7%, respectively). Conclusion In mild AD, most individual ADAS‐Cog memory and language items were reliable over time. Moreover, the scale captured additional information about the state, trait and accumulated effects of AD; the memory items tended to be more reflective of trait differences between subjects, whereas the language items tended to reflect effects that accumulated from one visit to the next, consistent with the typical progression of AD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".