Inability of healthy subjects to deposit potassium during hypokinesia and potassium supplementation
Bibliographic record
Abstract
OBJECTIVE: To determine the effect of potassium (K+) supplementation and hypokinesia (HK; diminished movement) on muscle K+ content and K+ loss. METHODS: Studies were conducted on 40 healthy male volunteers during a pre-experimental period of 30 days and an experimental-period of 364 days. Volunteers were equally divided into four groups: unsupplemented control subjects (UCS), unsupplemented experimental subjects (UES), supplemented control subjects (SCS), and supplemented experimental subjects (SES). A daily supplement of 1.17 mmol potassium-chloride (KCl) per kg body weight was given to the subjects in the SCS and SES groups. RESULTS: Muscle K+ content decreased (P < 0.05), and plasma K+ concentration, and K+ loss in urine and feces increased (P < 0.05) in the SES and UES groups compared with their pre-experimental levels and the values in their respective control groups (SCS and UCS). Muscle K+ content decreased more (P < 0.05), and plasma K+ concentration and K+ loss in urine and feces increased more (P < 0.05) in the SES group than in the UES group. CONCLUSION: Muscle K+ content is not decreased by the K+ deficient diet and K+ loss is not increased by the higher muscle K+ content in the body. Rather it is caused by the inability of the body to use K+ during HK and K+ supplementation.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".