Extreme hyperglycemia with ketoacidosis and hyperkalemia in a patient on chronic hemodialysis
Bibliographic record
Abstract
A patient on hemodialysis for end-stage renal disease secondary to diabetic nephropathy was admitted in a coma with Kussmaul breathing and hypertension (232/124 mmHg). She had extreme hyperglycemia (1884 mg/dL), acidosis (total CO(2) 4 mmol/L), hyperkalemia (7.2 mmol/L) with electrocardiographic abnormalities, and hypertonicity (330.7 mOsm/kg). Initial treatment with insulin drip resulted in a decrease in serum potassium to 5.3 mmol/L, but no significant change in mental status or other laboratory parameters. Hemodialysis of 1.75 hours resulted in rapid decline in serum glucose and tonicity and rapid improvement of the acidosis, but no change in mental status, which began to improve slowly after the hemodialysis was stopped, but with ongoing treatment with continuous insulin infusion. The rate of decline in tonicity during hemodialysis (14.5 mOsm/kg/h) was high, raising concerns about neurological complications. In this case, extreme hyperglycemia with ketoacidosis, hyperkalemia, and coma developing in a hemodialysis patient responded to insulin infusion. Monitoring of the clinical status and the pertinent laboratory values is required to assess the need for other therapeutic measures including volume and potassium replacement and emergency dialysis. The indications for and risks of emergency dialysis in this setting are not clearly defined.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".