Quality of Life of Patients with Type 1 Diabetes Mellitus Using Insulin Analog Glargine Compared with NPH Insulin: A Systematic Review and Policy Implications
Bibliographic record
Abstract
INTRODUCTION: Insulin analog glargine (GLA) has been available as one of the therapeutic options for patients with type 1 diabetes mellitus to enhance glycemic control. Studies have shown that a decrease in the frequency of hypoglycemic episodes improves the quality of life (QoL) of diabetic patients. However, there are appreciable acquisition cost differences between different insulins. Consequently, there is a need to assess their impact on QoL to provide future guidance to health authorities. METHOD: A systematic review of multiple databases including Medline, LILACS, Cochrane, and EMBASE databases with several combinations of agreed terms involving randomized controlled trials and cohorts, as well as manual searches and gray literature, was undertaken. The primary outcome measure was a change in QoL. The quality of the studies and the risk of bias was also assessed. RESULTS: Eight studies were eventually included in the systematic review out of 634 publications. Eight different QoL instruments were used (two generic, two mixed, and four specific), in which the Diabetes Treatment Satisfaction Questionnaire (DTSQ) was the most used. The systematic review did not consistently show any significant difference overall in QoL scores, whether as part of subsets or combined into a single score, with the use of GLA versus neutral protamine Hagedorn (NPH) insulin. Only in patient satisfaction measured by DTSQ was a better result consistently seen with GLA versus NPH insulin, but not using the Well-being Inquiry for Diabetics (WED) scale. However, none of the cohort studies scored a maximum on the Newcastle-Ottawa scale for quality, and they generally were of moderate quality with bias in the studies. CONCLUSION: There was no consistent difference in QoL or patient-reported outcomes when the findings from the eight studies were collated. In view of this, we believe the current price differential between GLA and NPH insulin in Brazil cannot be justified by these findings.
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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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".