Association of Antidiabetic Treatment with the Type of Obesity in Type 2 Diabetic Patients
Bibliographic record
Abstract
Objective: To find association between antidiabetic treatment and the type of obesity in type 2 diabetic patients. Methods: The study was conducted in National Institute of Diabetes & Endocrinology (NIDE), Karachi, over a period of 6 months, ie. from January to June, 2018. It was an observational analytical study, for which 59 patients were selected via non-probability sampling, as per inclusion and exclusion criteria. Data was collected through detailed history, examination. A database was developed and analyzed on SPSS 17. A p- value <0.05 was taken as statistically significant. Results: Fifty nine patients fulfilling the inclusion criteria were included in this study. While 30 (50.8%) had generalized obesity, 29 (49.2%) were not having generalized obesity. Further it was observed that 35 (59.3%) had abdominal obesity, while 24 (40.7%) were not having abdominal obesity. A total of 39 (66.1%) were on insulin, while 20 (43.9%) were not on insulin. Finally, 41 (69.5%) were on oral hypoglycemic drugs, while 18 (30.5%) were not on oral hypoglycemic drugs. P-values were not significant for the study parameters. Conclusion: There is no association between antidiabetic treatment and type of obesity in type 2 diabetic patients. Key Words: Diabetes, obesity, body mass index, insulin, oral hypoglycemic drugs How to Cite: Ali J, Ali S.S, Imran S, Tariq T, Mahmood U, Iqbal J. Association of antidiabetic treatment with the type of obesity in type 2 diabetic patients. Esculapio.2020;16(04):97-100.
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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 | 0.002 |
| 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 | 0.001 |
| 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".