Stability of Clinical Etiologic Diagnosis in Dementia and Mild Cognitive Impairment
Bibliographic record
Abstract
Many new therapies for dementia target a specific pathologic process and must be applied early. Selection of specific therapy is based on the clinical etiologic diagnosis. We sought to determine the stability of the clinical etiologic diagnosis over time and to identify factors associated with instability. We identified 4141 patients with dementia or mild cognitive impairment who made at least 2 visits approximately a year apart to a dementia research center, receiving a clinical etiologic diagnosis on each visit. We assessed concordance of etiologic diagnoses across visits, κ-statistics, and transition probabilities among diagnoses. The primary clinical etiologic diagnosis remained stable for 91% of patients but with a net shift toward dementia with Lewy bodies and Alzheimer's disease. Lower diagnostic stability was significantly associated with older age, nonwhite race, milder disease at presentation, more underlying conditions contributing to cognitive decline, lack of a consistent spouse/partner informant, and being evaluated by different clinicians on different visits. Multistate Markov modeling generally confirmed these associations. Clinical etiologic diagnoses were generally stable. However, several readily ascertained characteristics were associated with higher instability. These associations may be useful to clinicians for anticipating when an etiologic diagnosis may be more prone to future change.
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 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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".