Early Detection of Diabetic Neuropathy Based on Health Belief Model: A Scoping Review
Bibliographic record
Abstract
Abstract Introduction Uncontrolled blood sugar levels may result in complications, namely diabetic neuropathy. Diabetic neuropathy is a nerve disorder that causes symptoms of numbness, foot deformity, dry skin, and thickening of the feet. The severity of diabetic neuropathy carries the risk of developing diabetic ulcers and amputation. Early detection of diabetic neuropathy can prevent the risk of diabetic ulcers. Methods This research studied articles in 6 databases via Scopus, Ebsco, Pubmed, Sage journal, Science Direct, and Springer link with the keywords “screening Neuropathy” AND “Detection Neuropathy” AND “Scoring Neuropathy” AND “Diabetic” published in 2019-2023. This study identified articles based on the English language, variables, and population. This research used PRISMA Flow-Chart 2020 to record the article filtering process. Results This research identified articles through PRISMA Flow-Chart 2020, obtaining 25 articles that discussed early detection of diabetic neuropathy. Conclusion Early detection of diabetic neuropathy prevents the occurrence of diabetic neuropathy. The method for early detection of neuropathy is using a neuropathy questionnaire and physical examination. Neuropathy questionnaires are Michigan Neuropathy Screening Instrument (MNSI), Toronto Clinical Neuropathy Score (TCNS), and Diabetic Neuropathy Classification (BDC). Physical examination tools include biotesimeters, vibrations, neurotesimeters, tuning forks, monofilament threads, ultrasonography (USG) results, artificial intelligence (AI), and nomograms.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".