Clinical and Electrophysiological Characterization of Diabetic Neuropathy in a Sub‐Saharan African Cohort
Bibliographic record
Abstract
BACKGROUND: Diabetic neuropathy (DN) is the most frequent complication of diabetes mellitus, contributing to increased morbidity and mortality. Previous clinical studies on DN in sub-Saharan Africa (sSA) have used purely clinical approaches, potentially underestimating the true magnitude of this disease. This study was designed to determine the prevalence of definite diabetic neuropathy and describe the different subtypes using objective small and large fiber function measures. METHODS: This was a hospital-based cross-sectional study that included diabetes and prediabetes patients, followed up at Jordan Medical Services, Yaoundé, Cameroon, between March 2022 and February 2023. The "Toronto Clinical Neuropathy Score" and "Douleur Neuropathique en 4" questionnaires were used for clinical evaluation. Autonomic symptoms were equally recorded. Nerve conduction studies and Sudoscan were used for electrophysiological assessments of large and small fibre functions. RESULTS: Eighty-four participants were included; 91.7% had type 2 DM, 2.4% had type 1 DM, and 6% had glucose intolerance. DN was found in 73/84 (86.9%). Diabetic sensorimotor polyneuropathy (DSP) was the most frequent subtype (63.8%), followed by diabetic autonomic neuropathy (40.5%), mononeuropathy (36.9%), asymmetric axonal sensory neuropathy (4.8%) and treatment-induced neuropathy of diabetes (TIND) in 1.2% of patients. The prevalence of large and small fibre neuropathies was 38.1% and 25.0%, respectively. CONCLUSION: The prevalence of DN and specifically DSP in our study was higher than previously described in African literature. We identified subtypes never before reported in sSA, mainly small fibre neuropathy and TIND. This may have management and policy implications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".