The Occurrence, Trends, and Factors That Forecast Diabetic Peripheral Neuropathy: A Tertiary Centre Study
Bibliographic record
Abstract
Background: Diabetes is becoming increasingly prevalent worldwide, including in emerging countries. Diabetic peripheral neuropathy (DPN) is a common and potentially severe complication of diabetes, yet it has been relatively understudied, especially in developing nations. This study focuses on India, a country with a rapidly growing diabetic population, to determine the prevalence and factors associated with DPN. Methodology: It was a cross-sectional study conducted in 200 diagnosed cases of T2DM patients attending the OPD, inpatient in department of Medicine and Neurology, GSVM medical college and hospital, Kanpur, between April 2022 to April 2023 and assessed for peripheral neuropathy. Ethics committee approval was duly taken with the study conducted in accordance with the guidelines. Informed consent was taken from each participant. Inclusion criteria included prior diabetes diagnoses from government hospitals or registered medical practitioners, meeting ADA and WHO diagnostic criteria for new cases, BMI ≥ 27.5 kg/m² (as per WHO for Asians), and assessments for diabetic retinopathy or nephropathy. Exclusion criteria encompassed institutionalized individuals, data from unselected provinces, no prior diabetes diagnosis, and missing key variable data. The study involved 200 diabetic patients and assessed DPN using the Diabetic Neuropathy Symptom (DNS) Score and the Toronto Clinical Scoring System (TCSS). Logistic regression analysis was employed to identify factors associated with DPN. Results: The study found that 24.0% of the diabetic patients had DPN. Factors associated with DPN included gender, smoking, insulin treatment, diabetic retinopathy, and the presence of foot ulcers. Recommendations: (DPN) in emerging countries like India include raising awareness, implementing regular screening, improving diabetes care, promoting healthier lifestyles, ensuring better healthcare access, prioritizing research, and creating targeted treatment plans. Conclusion: This study sheds light on the prevalence and risk factors for DPN in India, an emerging country with a growing diabetes burden. Identifying these risk factors can contribute to improved treatment and prevention strategies for DPN in India and similar settings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".