Occurrence of Painful Diabetic Peripheral Neuropathy among Type 2 Diabetic Patients Attending a Tertiary Care Hospital
Bibliographic record
Abstract
Introduction: Diabetes affects 382 million people worldwide, and its prevalence is expected to increase to 592 million by the year 2035. Painful diabetic peripheral neuropathy (PDPN) is a common type of diabetic neuropathy and the most common cause of neuropathic pain. Hence, there is a need to access the incidence and the prevalence of this condition. The incidence of diabetes and its complications has increased to a greater extent among the rural population which have to be concentrated as there are lesser studies available for that population groups. Materials and Methods: Incidence is tested by a cross-sectional descriptive study consisting of two phases: Phase 1, an initial screening questionnaire including one question about pain; Phase 2, neurological history and examination using the Toronto Clinical Scoring System (TCSS). The observations thus obtained are evaluated with suitable statistics tool (SPSS version 16), and results are represented in the form of fig ures and graphs. Results: Total number of subjects were 100 of them 59 were male and 41 were female. Diabetic age of the individuals ranges between 2 and 19 years. The highest score with TCSS was 13 and least was 3. More than 86% of the subjects were suffering from peripheral neuropathy among which 46% are with mild and 35% with moderate and 5% with severe grades of DPN. Incidence of PDPN was increasing rapidly with the raising fasting blood sugar (FBS) levels and was 100% with FBS levels more than 261 mg/dl. Conclusions: PDPN incidence among the diabetic individuals is relatively high in the study population. Age does not influence the occurrence of PDPN among the diabetics while the diabetic age of the individuals has a highly significant relation with the occurrence of PDPN. The occurrence of PDPN was more among the individuals with high FBS levels which indicate the necessity of controlled glycemic levels in the prevention of complications associated with diabetes mellitus.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".