Laboratory Tests to Determine the Cause of Hypokalemia and Paralysis
Bibliographic record
Abstract
BACKGROUND: Hypokalemia and paralysis may be due to a short-term shift of potassium into cells in hypokalemic periodic paralysis (HPP) or due to a large deficit of potassium in non-HPP. Failure to make a distinction between HPP and non-HPP may lead to improper management. Therefore, we evaluated the diagnostic value of spot urine tests in patients with hypokalemia and paralysis during 3 years. METHODS: Before therapy, the urine potassium concentration, potassium-creatinine ratio, and transtubular potassium concentration gradient were determined in a second voided urine sample. RESULTS: Forty-three patients with hypokalemia and paralysis were identified: 30 had HPP and 13 had non-HPP. There was no significant difference in the plasma potassium or bicarbonate concentrations and in the pH of arterial blood between the 2 groups. All but 2 patients in the non-HPP group had urine potassium concentration values less than 20 mmol/L. Although the potassium concentration was significantly lower in the HPP group, there was some overlap. In contrast, the transtubular potassium concentration gradient and potassium-creatinine ratio differentiated patients with HPP vs non-HPP. Although only a mean +/- SD of 63 +/- 36 mmol of potassium chloride was administered in the patients with HPP, rebound hyperkalemia (>5 mmol/L) occurred in 19 (63%) of these 30 patients. CONCLUSIONS: Calculating the transtubular potassium concentration gradient and potassium-creatinine ratio provided a simple and reliable test to distinguish HPP from non-HPP. Minimal potassium chloride supplementation should be given to avoid rebound hyperkalemia in patients with HPP.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".