Co-administrating Subcutaneous Insulin Glargine in the Management of Diabetic Ketoacidosis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Background and Objective Diabetic ketoacidosis (DKA) is a serious and increasingly common complication of diabetes with high morbidity and mortality. Early use of long-acting basal insulin alongside IV insulin may improve outcomes. This systematic review and meta-analysis aims to evaluate the efficacy and safety of co-administering insulin glargine with IV insulin in DKA management. Methods A literature search was conducted to identify relevant studies. Key outcomes included time to DKA resolution, hospital stay length, and hypoglycemia risk. RCT quality was assessed using the Cochrane risk of bias tool, and retrospective studies with the Newcastle-Ottawa Scale. Standardized mean differences with 95% CIs were used for continuous outcomes, and risk ratios for dichotomous outcomes. A random-effects model was applied using RevMan (version 5.4). Results A total of six studies were included in this meta-analysis comprising of a cumulative sample size of 302 patients with 127 in the intervention group and 175 in the control group, the pooled results showed that co-administration of insulin glargine resulted in significantly reduced time to DKA resolution, along with decreased length of hospital stay as compared to the group receiving IV infusion alone, while the rate of hypoglycemia and hypokalemia were comparable between the two groups. Conclusion When treating DKA, a combination of IV insulin infusion and long-acting basal insulin glargine may shorten hospital stays and speed up the time to resolution without increasing the risk of hypoglycemia or hypokalemia.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".