Correlation of Serum Magnesium Levels with Microvascular Complications among Type 2 Diabetes patients in South India
Bibliographic record
Abstract
Background: Hypomagnesemia has been found to be associated with unfavorable effects on glucose homeostasis and insulin sensitivity in Type 2 Diabetes Mellitus (DM).If a relationship between levels of serum magnesium and microvascular complications can be established, it may form the basis for firther research on supplementation of magnesium in these patients.Aims: To assess the level of serum magnesium levels in Type 2 DM patients and to correlate serum magnesium concentration with microvascular complications in these patients.Methods: Cross sectional study in adult patients with type 2 diabetes presenting to a tertiary care center in semi-urban South India.Patients were subjected to history taking and detailed physical examination, including assessment of peripheral neuropathy by Toronto Clinical Neuropathy score.Basic investigations including HbA1c, Urine spot protein-creatinine ratio, as well as fundus examination for assessment of diabetic retinopathy were performed.Serum Magnesium level was analyzed in all patients, and its correlation with microvascular complications was computed by appropriate statistical methods.Results: A total of 105 patients meeting the inclusion criteria were recruited.Mean age of the study population was 56.92 ±11.14 years with mean duration of 8.18 ±4.88 years of diabetes, and mean HbA1c level of 11.014±2.12%.www.jmscr.igmpublication.orgImpact Factor (SJIF): 6.379
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".