Comparative Glycemic Effectiveness of Long- and Rapid-Acting Insulin in Patients with Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Insulin therapy is essential for managing type 2 diabetes mellitus (T2DM), particularly in patients who fail to achieve glycemic targets with oral antidiabetic agents. Long-acting insulin is primarily used to control basal glucose levels, while rapid-acting insulin targets postprandial hyperglycemia. However, comparative real-world evidence regarding their effectiveness on glycated hemoglobin (HbA1c) and fasting blood glucose (FBG) remains limited. This study aimed to evaluate and compare the effectiveness of long-acting and rapid-acting insulin in improving HbA1c and FBG levels among patients with T2DM. A retrospective before–and–after observational study was conducted involving 122 T2DM patients treated at the outpatient unit of Majalaya Regional General Hospital between January and December 2024. Patients received either long-acting insulin (e.g., insulin glargine) or rapid-acting insulin (e.g., insulin lispro and insulin aspart) as monotherapy. Changes in HbA1c and FBG before and after therapy were analyzed using paired t-tests or Wilcoxon signed-rank tests. Clinical effectiveness was defined according to American Diabetes Association criteria as a reduction of ≥1% in HbA1c or ≥30 mg/dL in FBG. Insulin therapy significantly reduced HbA1c (−7.77 ± 3.09, p < 0.001) and FBG levels (Z = −5.53, p < 0.001). Based on ADA criteria, 90.3% of patients achieved an effective reduction in HbA1c, while 43.5% achieved an effective reduction in FBG. Insulin lispro and insulin glargine showed the highest HbA1c-based effectiveness (100%), whereas FBG-based effectiveness varied across formulations. Insulin therapy significantly improves long-term and short-term glycemic control in T2DM patients, with insulin lispro and insulin glargine demonstrating the most consistent effectiveness.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".