Delayed Parkinson’s Disease Diagnosis among African-Americans: The Role of Reporting of Disability
Bibliographic record
Abstract
BACKGROUND/AIMS: Racial differences in the observed prevalence of Parkinson's disease (PD) may be due to delayed diagnosis among African-Americans. We sought to compare the stage at which African-American and white PD patients present for healthcare, and determine whether perception of disability accounts for racial differences. METHODS: Using records of veterans with newly diagnosed PD at the Philadelphia Veterans Affairs Medical Center, we calculated differences in reporting of symptoms as the difference in z-scores on the Unified Parkinson Disease Rating Scale part 2 (disability) and part 3 (motor impairment). Ordinal logistic regression was used to determine predictors of stage at diagnosis. RESULTS: African-American (n = 16) and white (n = 58) veterans with a mean age of 70.1 years were identified. African-Americans presented at a later PD stage than whites (median Hoehn + Yahr stage 2.5 vs. 2.0, p = 0.02) and were more likely to under-report disability relative to motor impairment (81 vs. 40%, p < 0.01). Multivariate analysis showed that under-reporting of disability accounted for much of the effect of race on stage of diagnosis. CONCLUSIONS: Under-reporting of disability among African-Americans may account for later stages of PD diagnosis than whites. This study begins to explain the mechanisms underlying observed racial disparities in PD.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".