Bibliographic record
Abstract
Hyperkalemia can be a life-threatening event due to the risk of potentially fatal arrhythmias. Hyperkalemia has been reported in 1.3% (serum potassium greater than 6.0 mEq/mL) to 10% (greater than 5.3 mEq/mL) of patients. Hyperkalemia secondary to beta-adrenergic receptor blockade can occur in 1% to 5% of patients and is more likely to occur in non-cardio-selective beta-blockers versus cardio-selective beta-blockers. This case report describes hyperkalemia in a 72-year-old female with diabetes and underlying chronic renal failure receiving metoprolol. Chronically, potassium balance is maintained by the kidney. In acute situations, such as a larger than normal potassium load, both the kidney and the body's cells react to maintain normal potassium levels. Generally, hyperkalemia occurs secondary to 3 mechanisms: excessive potassium intake, disturbed cellular uptake of potassium, or impaired renal excretion of potassium. Beta-blockers, when used in patients with comorbidities such as renal dysfunction or insulin insufficiency, can potentially cause hyperkalemia. As demonstrated in this case report, hyperkalemia can occur in patients treated with cardio-selective beta-blockers with concurrent risk factors. Health care professionals need to be aware of this potentially life-threatening event to effectively prevent occurrences of beta-blocker-induced hyperkalemia.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".