Bolus Administration of Intravenous Glucose in the Treatment of Hyperkalemia: A Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Hyperkalemia is a common medical emergency that may result in serious cardiac arrhythmias. Standard therapy with insulin plus glucose reliably lowers the serum potassium concentration ([K(+)]) but carries the risk of hypoglycemia. This study examined whether an intravenous glucose-only bolus lowers serum [K(+)] in stable, nondiabetic, hyperkalemic patients and compared this intervention with insulin-plus-glucose therapy. METHODS: A randomized, crossover study was conducted in 10 chronic hemodialysis patients who were prone to hyperkalemia. Administration of 10 units of insulin with 100 ml of 50% glucose (50 g) was compared with the administration of 100 ml of 50% glucose only. Serum [K(+)] was measured up to 60 min. Patients were monitored for hypoglycemia and EKG changes. RESULTS: Baseline serum [K(+)] was 6.01 ± 0.87 and 6.23 ± 1.20 mmol/l in the insulin and glucose-only groups, respectively (p = 0.45). At 60 min, the glucose-only group had a fall in [K(+)] of 0.50 ± 0.31 mmol/l (p < 0.001). In the insulin group, there was a fall of 0.83 ± 0.53 mmol/l at 60 min (p < 0.001) and a lower serum [K(+)] at that time compared to the glucose-only group (5.18 ± 0.76 vs. 5.73 ± 1.12 mmol/l, respectively; p = 0.01). In the glucose-only group, the glucose area under the curve (AUC) was greater and the insulin AUC was smaller. Two patients in the insulin group developed hypoglycemia. CONCLUSION: Infusion of a glucose-only bolus caused a clinically significant decrease in serum [K(+)] without any episodes of hypoglycemia.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".