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Prevalence of Peripheral Neuropathy: A Cross Sectional Study among Diabetic Patients

2025· article· en· W4408528792 on OpenAlexaboutno aff
T. Suja, Santhi Appavu, D Sasikala

Bibliographic record

VenueRESEARCH REVIEW International Journal of Multidisciplinary · 2025
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsCross-sectional studyMedicinePeripheral neuropathyDiabetes mellitusPeripheralInternal medicineDiabetic neuropathyPathologyEndocrinology

Abstract

fetched live from OpenAlex

Diabetes is a growing challenge in India with an estimated 8.7% diabetic patients between 20 and 70 years of age. With the rising prevalence of diabetes mellitus, there is an increasing number of people with complications of Diabetes. Neuropathy is the most common complication of Diabetes and it affects approximately half of all diabetic patients. Methods: A cross sectional study was conducted to assess the prevalence of Peripheral Neuropathy among 86 Diabetic patients selected by cluster sampling technique residing in selected wards of Thiruvattar Panchayat. The selected sample were screened for peripheral neuropathy using Toronto Clinical Neuropathy Score (TCNS). Results: The findings of the study revealed that, totally 38.4% Diabetic patients had Peripheral Neuropathy. Among them, 16.3% of diabetic patients had mild Neuropathy, 12.8% of diabetic patients had moderate Neuropathy and 9.3% of diabetic patients had severe Neuropathy. There was significant association of peripheral neuropathy with selected clinical variables such as duration of Diabetes Mellitus and Fasting Blood Sugar (p<0.000). Conclusion: The prevalence of Diabetic Peripheral Neuropathy was higher among Diabetes Mellitus patients and it has negative impact on the progression of Diabetes Mellitus.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.064
GPT teacher head0.454
Teacher spread0.391 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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