Ethnic Disparities in Clinical Presentation of Patients with Early-Onset Alzheimer’s Disease (P14-6.002)
Bibliographic record
Abstract
Objective: N/A Background: Early-onset Alzheimer’s Disease (EOAD) affects patients younger than age 65 and may present with non-amnestic features. Black patients are half as likely to be diagnosed despite twice as many risk factors. Ethnic disparities in EOAD have not been fully characterized. Design/Methods: We retrospectively identified EOAD patients February 2017–February 2022 and collected data on ethnicity, level of education, Montreal Cognitive Assessment (MoCA) scores and clinical presentation. Results: Of 88 total patients, 60 (68.8%) identified as Caucasian/White and 28 (31.8%) identified as African American/Black. Age at diagnosis (years) was (57.3 ± 5.24) in Black patients compared to (55.9 ± 5.69) in White patients. Time to diagnosis (years) was on average longer in Black patients (5 ± 9.21) compared with White patients (3.5 ± 6.44) and Black patients were on average more advanced (total MOCA scores) at presentation (12.17 ± 6.91) compared with White patients (15.15 ± 5.92) despite similar education level (in years) among Black patients (14.71 ± 2.30) and White patients (15.21±3.00). An amnestic profile was more common in Black patients (19 or 67.9%) than non-amnestic presentations (9 or 32.1%), although 2 patients (7.14%) presented with visual symptoms suggestive of posterior cortical atrophy (PCA), 5 (17.9%) patients had prominent language symptoms suggestive of logopenic primary progress aphasia (l-PPA) and 1 (3.6%) patient had an executive presentation, suggestive of frontal variant of AD (fAD). Comparatively, non-amnestic presentations were more commonly diagnosed in White patients, with 11 (18.3%) PCA diagnoses, 10 (16.7%) patients with fAD and 8 (13.3%) patients with l-PPA. Conclusions: In our cohort, Black patients with EOAD are on average older, more advanced and experience longer delays in diagnosis than their White counterparts. Non-amnestic presentations are more common in White patients. Further work is needed to further characterize the barriers to diagnosis accessibility of care for Black patients with EOAD. Disclosure: Ms. Yang has nothing to disclose. Dr. Yoon has nothing to disclose. Ms. Covaciu has nothing to disclose. The institution of Dr. Seibert has received research support from Retirement Research Foundation. The institution of Dr. Seibert has received research support from Center for Healthcare Delivery Science and Innovation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".