Underestimating the risk of developing Chronic Kidney Diseases Among the Jordanian Diabetic Patients
Bibliographic record
Abstract
Abstract Background : Despite the increase in Type 2 Diabetes Mellites (T2DM) prevalence in Jordan, there is a lack of studies on kidney function status among the T2DM patients. The aim of this study is to give an insight into kidney functions in T2DM patients and to identify patients at risk of renal diseases. Methods: A cross-sectional study was undertaken at King Abdullah University Hospital in Jordan. The Modification of Diet in Renal Disease (MDRD) formula was used to calculate the estimated glomerular filtration rate (eGFR). The data were analyzed using the SPSS software. Results: 22.3% of the T2DM patients had eGFR values less than 60 mL/min per 1.73 m 2 . The decrease in eGFR was accompanied by an increase in proteinuria (PU) and albumin to creatinine ratio (ACR). Most of the T2DM patients were in Stage 2 CKD. The eGFR values were lower in males than females. The correlation analysis showed that eGFR values were negatively correlated with the duration of T2DM, serum creatinine (sCr), ACR, and PU, reflecting a decrease in kidney functions with the increase in T2DM duration, and patients' biochemical variables. Most of the T2DM patients had high blood sugar, urine creatinine, and evidence of protein in the urine samples, indicating uncontrolled diabetes. Conclusions: The eGFR value can be used in addition to the biochemical variables to better identify T2DM patients at risk of developing chronic kidney diseases in the future, especially in T2DM patients with poor glycemic control.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".