Diagnosis of Severity of Alzheimer’s Disease from Clinical notes in the Veterans Affairs Healthcare System
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease (AD) typically progresses in three stages: mild, moderate, and severe. Early detection improves patient care related to identification of suitable interventions and end‐of‐life planning. However, AD severity diagnosis is uncommon. The aim of this study is to identify Veterans in various stages of AD using clinical notes in the Veterans Affairs Healthcare System. Method Notes containing “Alz*” from fiscal year 2019 were identified then screened for information indicating a clinical diagnosis of probable AD. Screened notes with AD severity keywords (mild, moderate, severe, early, late, advanced) within four words of “Alz*” were considered as a severity diagnosis. Notes containing a single AD severity diagnosis were included in the study. Result 141,816 Veterans with 547,318 notes containing “Alz*” were identified as probable AD cases. Roughly 9% of notes contained at least one severity keyword and 6% of notes contained a single severity keyword. 34,181 notes with a single severity keyword from 14,148 Veterans were included in this study. The cohort comprised of 96% males and 78% identified as White with a mean age of 79. Ten percent of Veterans with an AD note included severity diagnosis. Approximately 19%, 12%, 18%, 20%, 3%, and 28% of severity notes contained mild, moderate, severe, early, late, and advanced, respectively. The mean age for Veterans with a mild, moderate, or severe AD diagnosis increased from 78 to 81. The mean age for early, late, and advanced AD patients increased from 74, 79, and 81, respectively. AD severity diagnosis was found in notes related to primary care (11%), internal medicine (10%), psychiatry (8%), mental health (7%), neurology (6%), geriatric (5%), neuropsychology (1%), and psychology (1%). Conclusion AD severity diagnosis is uncommon. Identification of patients in various stages of AD will lead to a better understanding of transition from one severity to another and facilitate the coordination of services to improve patient care. Specifically, identification of AD patients in the early stage may identify candidates for novel therapeutics focused on delaying AD progression.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".