Prevalence and Risk Factors of Distal Symmetric Polyneuropathy Among Predominantly Non-Hispanic Black, Low-Income Patients
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Distal symmetric polyneuropathy (DSP) is a disabling, often painful condition associated with falls and reduced quality of life. Non-Hispanic Black people and people with low income are underrepresented in existing DSP studies; therefore, it is unknown whether data accurately reflect the prevalence, risk factors, and burden of disease in these populations. METHODS: Patients older than 40 years presenting to an outpatient internal medicine clinic predominantly serving Medicaid patients in Flint, Michigan, were enrolled in a cross-sectional study. Demographics, clinical characteristics, including medication use, anthropomorphic measurements, fasting lipids, and hemoglobin A1c were collected. DSP was defined using the modified Toronto Clinical Neuropathy Score (mTCNS). Multivariable logistic regression was performed to model DSP and undiagnosed DSP as a function of potential risk factors age, metabolic syndrome, and race. DSP burden was measured using Peripheral Neuropathy Quality of Life Instrument-97. RESULTS: < 0.001). DISCUSSION: DSP is extremely common and often underrecognized in this predominantly non-Hispanic Black, low-income population and leads to substantial disease burden. Metabolic syndrome is a highly prevalent, modifiable risk factor in this population that should be managed to lower DSP prevalence.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".