Risk Factors Associated with Diabetes Mellitus in Local Population of Lahore, Pakistan
Bibliographic record
Abstract
BACKGROUND: Diabetes is the leading cause of morbidity and mortality amongst the people of Pakistan. In 2015, 7 million people had diabetes and the number is still on raise. Family history of diabetes, high body mass index, and other sociodemographic factors are the risk factors of diabetes. Persistent exposure to excessive glucose may be a reason behind diabetic complications like nephropathy, neuropathy, retinopathy, cardiomyopathy and gestational diabetes mellitus.METHODS: For the evaluation of laboratory parameters, 600 blood samples were collected at Akhuwat Diabetic Centre and from Jinnah Hospital, Lahore. Demographic data of the participants was collected by filling a questionnaire. Lipid profile, liver enzymes, and renal function tests were performed and statistical analysis was done.RESULTS: Type 2 diabetes mellitus among other types is the most prevalent form of diabetes in our population. Family history of diabetes (p=0.002), Body Mass Index (>25) p<0.001, high cholesterol (p=0.04), high triglyceride p<0.001, high LDL p<0.001 and low HDL p<0.001 are significantly associated with the incidence of diabetes. Hypertension among the other comorbidities is more common in diabetic patients.CONCLUSION: Type 2 Diabetes Mellitus is highly prevalent in the local population. Improved lifestyle and proper medical monitoring can help to manage diabetes in our population.
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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".