Prevalence and Risk Factors for Neuropathy in a Canadian First Nation Community
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to determine the prevalence of and risk factors for diabetic neuropathy in a Canadian First Nation population. RESEARCH DESIGN AND METHODS: This was a community-based screening study of 483 adults. Measures included glucose, A1C, cholesterol, triglycerides, homocysteine, hypertension, waist circumference, height, weight, and foot examinations. Neuropathy was defined as loss of protective sensation determined through application of a 10-g monofilament. RESULTS: Twenty-two percent of participants had a previous diagnosis of diabetes, and 14% had new diabetes or impaired fasting glucose (IFG). The prevalence of neuropathy increased by glucose level: 5% among those with normal glucose levels, 8% among those with new IFG and diabetes, and 15% among those with established diabetes (P < 0.01). Those with neuropathy were more likely to have foot deformities (P < 0.01) and callus (P < 0.001) than those without neuropathy. Among those with dysglycemia (>or=6.1 mmol/l), the mean number of foot problems for those with insensate feet was 3 compared with 0.3 among those with sensation (P < 0.001). In multivariate logistic regression female sex, low education, A1C, smoking, and homocysteine were independently associated with neuropathy, after controls for age. CONCLUSIONS: Neuropathy prevalence is high, given the young age of our participants (mean 40 years) and was present among those with undiagnosed diabetes. The high number and type of foot problems places this population at increased risk for ulceration; the low level of foot care in the community increases the risk. Homocysteine is a risk factor that may be related to lifestyle and requires further investigation.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".