Extent and Predictors of Poor Glycaemic Control among Elderly Pakistani Patients with Type 2 Diabetes Mellitus: A Multi-Centre Cross-Sectional Study
Bibliographic record
Abstract
Objectives: This study aimed to explore the relationship between glycaemic control and factors that may influence this among elderly type 2 diabetes mellitus (T2DM) patients in Lahore, Pakistan. Methods: This descriptive, cross-sectional study was conducted at the Jinnah and Sir Ganga Ram Hospitals, Lahore using convenience sampling techniques between 1 December 2015 and 28 February 2016. The sample consisted of elderly (>65 years) T2DM patients. Glycaemic values and patient characteristics were obtained from medical charts. Consenting patients were interviewed to complete the Barthel Index, Lawton Instrumental Activities of Daily Living Scale, Clinical Frailty Scale, Iowa Pain Thermometer Scale, Geriatric Depression Scale, Montreal Cognitive Assessment tool, Mini Nutritional Assessment Scale—Short Form and Self Care Inventory—Revised Version. Multiple logistic regression analysis was carried out to determine the predictors of poor glycaemic control. Results: A total of 490 patients were approached and 400 agreed to participate. Overall, nearly one-third (32.2%, n = 129) of patients had glycated haemoglobin (HbA1c) at the target level. Fasting and random plasma glucose levels were within the target range to much the same extent; (36.8%, n = 147) and (27%, n = 108), respectively. HbA1c levels were also higher in patients with co-morbidities (67.4%, n = 229) with diabetes-related complications (73.5%, n = 227). Significant predictors of impaired glycaemic control (HbA1c) included poor diabetes self-care (adjusted odds ratio (AOR) 0.96; 95% confidence interval (CI) 0.95, 0.98), not being prescribed oral hypoglycaemic agents (OHA) (AOR 6.22; 95% CI 2.09, 18.46), regular hypoglycaemic attacks (AOR 2.53; 95% CI 1.34, 4.81) and falling tendency (AOR 0.19; 95% CI 0.10, 0.36). Conclusions: Poor glycaemic control prevailed among the majority of elderly Pakistani diabetic patients in this study. Triggering factors of poor glycaemic control should be taken into consideration by the healthcare professionals in targeting multifaceted interventions to achieve good glycaemic control.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".