Prevalence of Diabetic Peripheral Neuropathy and Its Association With Vitamin B12 Deficiency and Health-Related Quality of Life
Bibliographic record
Abstract
Background: The aim of the study was to determine the prevalence of diabetic peripheral neuropathy and vitamin B12 deficiency, their associated factors, and impact on health-related quality of life in a sample from the Dominican Republic. Methods: A total of 367 patients with diabetes were recruited. The Toronto Clinical Scoring System (TCSS) was used to evaluate the presence of neuropathy symptoms, while the 36-Item Short Form Health Survey (SF-36) was used to assess health-related quality of life. Results: Of the participants, 33.5% had neuropathy according to TCSS scores. Only one participant reported having a previous diagnosis of neuropathy. Factors associated with neuropathy were age, chronic renal insufficiency, and history of stroke. The prevalence of vitamin B12 deficiency was 4.5%, and no association was found with neuropathy. The presence of neuropathy significantly affected (P < 0.05) all physical dimensions of the SF-36 and the vitality dimension. Factors associated with the Physical Component Score (PCS) were age, neuropathy, and total number of comorbidities; factors associated with Mental Component Score (MCS) were age, sex, and being widowed. Conclusions: The prevalence of neuropathy was high and its impact on QoL was significant. Almost none of the patients in which neuropathy was detected had a previous diagnosis. Considering this, the development of awareness and prevention interventions among both doctors and patients in the Dominican context is of utmost importance.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".