To Bind or to Let Loose: Effectiveness of Sodium Polystyrene Sulfonate in Decreasing Serum Potassium
Bibliographic record
Abstract
Background. The use of sodium polystyrene sulfonate in decreasing serum potassium has recently been questioned due to the lack of documented effectiveness. Methods. A retrospective cohort analysis of all hospitalized patients who received sodium polystyrene sulfonate over four months was performed. The change in serum potassium was noted over a period of 24 hours. Patients who received any other form of potassium-altering drug or treatment were excluded. Results. The administration of sodium polystyrene sulfonate reduced serum potassium by 16.7% (P < 0.001) as compared to the baseline serum potassium over a period of 24 hours. During this same time, no change in serum creatinine was identified (P = 0.73). In addition, there was no correlation between potassium and creatinine change (r(2) = 0.0004 and P = 0.99). Patients with higher initial serum potassium (≥5.6 mEq/L) reduced their potassium concentration 4% more than those with initial serum potassium of <5.6 mEq/L; however, this reduction did not reach statistical significance (P = 0.32). There was no significant difference in the effectiveness of 15 gm and 30 gm resin preparation (P = 0.54). Thirteen deaths were noted in our cohort, of which one death was due to ischemic colitis. Conclusion. We conclude that sodium polystyrene sulfonate is effective in lowering serum potassium.
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 | 0.001 |
| 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".