Effectiveness, safety, initial optimal dose, and optimal maintenance dose range of basal insulin regimens for type 2 diabetes: A systematic review with meta‐analysis
Bibliographic record
Abstract
AIMS: To investigate the effectiveness, safety, optimal starting dose, optimal maintenance dose range, and target fasting plasma glucose of five basal insulins in insulin-naïve patients with type 2 diabetes mellitus. METHODS: MEDLINE, EMBASE, Web of Science, and the Cochrane Library were searched from January 2000 to February 2022. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed and the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach was adopted. The registration ID is CRD42022319078 in PROSPERO. RESULTS: Among 11 163 citations retrieved, 35 publications met the planned criteria. From meta-analyses and network meta-analyses, we found that when injecting basal insulin regimens at bedtime, the optimal choice in order of most to least effective might be glargine U-300 or degludec U-100, glargine U-100 or detemir, followed by neutral protamine hagedorn (NPH). Injecting glargine U-100 in the morning may be more effective (ie, more patients archiving glycated hemoglobin < 7.0%) and lead to fewer hypoglycemic events than injecting it at bedtime. The optimal starting dose for the initiation of any basal insulins can be 0.10-0.20 U/kg/day. There is no eligible evidence to investigate the optimal maintenance dose for basal insulins. CONCLUSIONS: The five basal insulins are effective for the target population. Glargine U-300, degludec U-100, glargine U-100, and detemir lead to fewer hypoglycemic events than NPH without compromising 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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.044 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".