Reduced alternative insulin dosing in hyperkalemia: A meta‐analysis of effects on hypoglycemia and potassium reduction
Bibliographic record
Abstract
Abstract Study Objective Recent studies have identified that reduced alternative intravenous insulin doses, such as 5 units or 0.1 units/kg, may reduce the risk of hypoglycemia compared to standard doses of 10 units in patients treated for hyperkalemia. However, some studies suggest that these alternative doses may reduce the ability to lower serum potassium. This study was performed to determine the impact of alternative insulin dosing on hypoglycemia and potassium reduction in patients with hyperkalemia. Design Meta‐analysis. Data Source PubMed/MEDLINE, CENTRAL, Ovid, and ClinicalTrials.gov were searched from inception through November 2020. Patients Patients treated with standard (10 units) or alternative (<10 units) insulin dosing strategies for hyperkalemia. Only studies that evaluated hypoglycemia (serum glucose <70 mg/dl), severe hypoglycemia (serum glucose <50 mg/dl), and potassium reduction post‐treatment were included in the meta‐analysis. All articles were assessed for bias using the Cochrane Risk of Bias Assessment Tool and Newcastle‐Ottawa scales for randomized prospective trials and retrospective trials, respectively. Intervention None. Measurements and Main Results Ten retrospective cohort studies ( n = 3437) were included and had low‐ or moderate‐risk of bias. Alternative insulin dosing strategies included 5 units, 0.1 units/kg, and <10 units. Alternative dosing had lower pooled odds of hypoglycemia (odds ratio [OR] 0.55, 95% confidence interval [CI] 0.43–0.69, I 2 = 8%) and severe hypoglycemia (OR 0.41, 95% CI 0.27–0.64, I 2 = 0%). No difference in potassium reduction was detected (mean difference −0.02 mmol/L, 95% CI −0.11–0.07, I 2 = 53%). Conclusions Alternative insulin dosing strategies for hyperkalemia management resulted in less hypoglycemia and severe hypoglycemia without compromising potassium reduction compared to standard dose. Prospective studies are needed to confirm 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 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.056 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".