Dysmagnesemia in critically ill diarrheal patients in Bangladesh
Bibliographic record
Abstract
BACKGROUND: Despite having a pivotal role in numerous physiologic functions, magnesium disorders are rarely considered in clinical practice. This study aimed to explore the burden, predictors, and outcomes associated with magnesium imbalances among critically ill patients admitted to critical care settings with diarrheal disease. METHODS: A retrospective chart analysis was done among critically ill patients with diarrhea aged more than 18 years admitted to the Intensive Care Unit (ICU) of a specialized hospital who had their serum magnesium measured. Data were extracted from an electronic health record system. Serum magnesium levels were measured upon ICU admission. Multivariate multinomial logistic regression analysis was done to find out the associations with clinical variables. RESULTS: There was a higher incidence of hypomagnesemia (34%) than hypermagnesia (5.9%). On multivariate analysis, there were independent associations of hypomagnesemia with sepsis (mOR=6.25, 95% CI: 3.61 to 10.81, p < 0.001), H/O regular medicine intake prior admission (mOR=1.94, 95% CI: 1.18 to 3.18, p = 0.01). On the other hand, hypermagnesemia was independently associated with dehydration (mOR=4.78, p = 0.003, 95% CI: 1.6 to 14.3). Comparing with other electrolyte disorders, hypocalcemia (p < 0.001) was associated with hypomagnesemia. Hypermagnesemia was associated with hypochloremia (p = 0.017), metabolic acidosis (p = 0.014), and hypercalcemia (p = 0.002). CONCLUSION: The high occurrence of dysmagnesemia in our study highlights the need to closely monitor magnesium in critically ill ICU patients, particularly in resource limited settings. This could help prevent serious complications related to magnesium imbalances. Intensivists should remain alert to magnesium disturbances and conduct thorough patient evaluations.
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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 | 0.001 | 0.001 |
| 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".