Hypokalemia, hypomagnesemia, and hyponatremia are associated with acute kidney injury in patients treated with cisplatin
Bibliographic record
Abstract
Introduction Cisplatin-associated acute kidney injury (C-AKI) is common. Predictive factors include age >60 years, hypertension, cisplatin dose, diabetes, and serum albumin < 3.5 g/L. The association between C-AKI and hypokalemia, hypomagnesemia or hyponatremia has not been well characterized. Methods Data from a previous retrospective observational study was obtained. Patients were separated into three groups with similar cisplatin doses and schedules. Group A received cisplatin 60–100 mg/m 2 every three weeks with laboratory assessments before treatment, group B received cisplatin 60–75 mg/m 2 every three weeks with laboratory assessments before days 1 and 8 and group C had weekly cisplatin 40 mg/m 2 with weekly laboratories assessments. The association between hypomagnesemia, hypokalemia, hyponatremia, and risk of AKI was determined using a counting process specification of Cox's regression models. Results A total of 1301 patients were separated into groups A ( n = 713), B ( n = 204), and C ( n = 384). The proportion of patients with at least one event of hypokalemia, hypomagnesemia, or hyponatremia was lower in group A (29.2%, 57.6%, 36.2%) compared to groups B (43.6%, 67.2%, 59.8%) and C (49.0%, 78.7%, 51.0%). The incidence of all grade C-AKI was 35.6% (group A), 46.6% (group B), and 18.2% (group C). In group A, the risk of AKI doubled with hyponatremia or hypomagnesemia and tripled with hypokalemia. This association was not seen with other groups. Conclusion Among patients with the highest doses of cisplatin, the presence of one electrolyte disorder was associated with an increased risk of C-AKI. Other studies are needed to characterize the presence of an electrolyte disorder as a predictive risk factor of C-AKI in this subpopulation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".