Laboratory Tests to Determine the Cause of Hypokalemia and Paralysis
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it