The Effect of Uric Acid as a Predisposing Factor on Polyneuropathy in Patients with Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Background: Since serum uric acid is a controllable and modifiable factor in diabetic patients, identifying the risk factors and accelerating the incidence of neuropathy in these patients plays an important role, and can reduce its level, and the patient's disability, as well as additional therapeutic costs for the patient and the health system in the country. Method: In this retrospective cohort study conducted at the Golestan Hospital in 2015-2017, the study population was 100 type 2 diabetic patients based on NCS of 54 patients with polyneuropathy. First, the demographic data on clinical examinations, lab tests, and uric acid levels in these patients were recorded on a checklist. Then, in 2017, patients were reassessed for clinical investigations and lab tests, and all data entered on the previous checklist. Finally, all the data were analyzed using the SPSS v23. Results: The mean age of patients with polyneuropathy was 51.77 years, and there was a significant relationship between age, BMI and duration of diabetes with neuropathy, but there was no significant difference in gender, smoking and hypertension. The mean serum level of uric acid in the two years ago was 3.85 mg/dl, and at the time of the study, it was 4.18±1.55 mg/dl. There was no significant difference in serum levels of this substance after two years of follow up in patients with polyneuropathy (P=0.139). The incidence of polyneuropathy was reported by NCS findings of 54%. In other words, 54% of diabetic patients developed diabetic polyneuropathy for two years. Conclusion: Polyneuropathy is a common complication in diabetic patients, and the serum levels of uric acid over time cannot have a significant effect on the incidence of this disorder.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".