Impact of Disease-Specific Counseling on Quality of Life and Glycemic Control in Different Types of Diabetes
Bibliographic record
Abstract
Background: Diabetes mellitus (DM) is associated with metabolic complications and impaired health-related quality of life (HRQoL). While pharmacological therapy remains central to management, structured counseling may provide additional benefits. This study evaluated the impact of counseling on glycemic control and HRQoL among individuals with type 1 diabetes mellitus (T1DM), type 2 diabetes mellitus (T2DM), and pancreatic DM. Methods: This prospective interventional study included 150 adults with diabetes (T1DM: 30%, T2DM: 54%, pancreatic DM: 16%). Baseline assessments comprised glycemic parameters and HRQoL, measured using the Short Form-36 Health Survey (SF-36) questionnaire. Participants received individualized counseling on diet, exercise, medication adherence, and self-care, with follow-up assessments after 3 months. Results: Significant improvements were observed in glycemic control and HRQoL. Mean glycated hemoglobin (HbA1c) decreased from 11.06±2.15% to 10.52±2.55% (P = 0.002), and random blood sugar declined from 303.9 ± 29.8 mg/dL to 263.3 ± 60.5 mg/dL (P < 0.001). The SF-36 Physical Component Score improved from 37.4 ± 12.8 to 39.4 ± 13.3 (P < 0.001), and the SF-36 Mental Component Score increased from 29.1 ± 13.3 to 33.1 ± 12.7 (P < 0.001). The greatest improvements were observed in T2DM, whereas pancreatic DM patients showed comparatively modest gains. Conclusions: Structured, disease-specific counseling significantly enhanced both glycemic control and SF-36-derived HRQoL scores. These findings emphasize the importance of integrating counseling interventions into routine, patient-centered diabetes care.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".