Clinical Staging of Alzheimer’s Disease: Concordance of Subjective and Objective Assessments in the Veteran’s Affairs Healthcare System
Bibliographic record
Abstract
INTRODUCTION: Uncertainty surrounding the accurate assessment of the early-stage Alzheimer's disease (AD) may cause delayed care and inappropriate patient access to new AD therapies. METHODS: To analyze clinical assessments of patients with AD in the Veteran's Affairs (VA) Healthcare System and evaluate concordance between subjective and objective assessments, we processed clinical notes extracted by text integration utilities between April 1, 2008 and October 14, 2021. Veterans who had mild, moderate, or severe AD with clinical notes documenting both clinician's judgement of AD severity and objective test scores from the Mini-Mental State Examination or the Montreal Cognitive Assessment were included. Using clinician-defined severity cohorts, we determined concordance between the clinician's (subjective) assessments and the test-derived (objective) assessments of AD severity. Concordance was assessed over time and by selected symptoms and comorbidities, as well as healthcare system factors. RESULTS: A total of 8888 notes were initially extracted; the final analysis sample included 7514 notes corresponding to 4469 unique patients (mean [standard deviation] age of 78 [9] years; 96.5% male; 77.8% White). Subjective and objective assessments were concordant in approximately half (53%) of overall notes. In the mild Alzheimer's cohort, patients were assessed to have more severe disease by objective test scores in 40% of notes. Concordance varied about 21-73%, 47-58%, and 40-64% across symptoms/comorbidities, clinician types, and Veteran's Integrated Service Networks, respectively. The proportion of concordant notes was higher in visits to dementia (61%) instead of non-dementia clinics (53%). CONCLUSIONS: We found higher concordance between clinician's assessment and test-based assessment of Alzheimer's disease severity in dementia specialty clinics. Discordance is especially high for the subjectively assessed mild AD cohort where objective assessments showed a higher severity level in 40% of notes. These data indicate a critical need for improved understanding of clinical assessments and decision-making to identify appropriate patients for anti-amyloid therapy.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".