Lower Incidence of Hypo-Magnesemia in Surgical Intensive Care Unit Patients in 2011 Versus 2001
Bibliographic record
Abstract
BACKGROUND: Hypo-magnesemia is described to occur in as many as 65% of intensive care unit (ICU) patients. Magnesium (Mg) is a cofactor in over 300 enzymatic reactions involving energy metabolism, protein, and nucleic acid synthesis. The membrane pump that creates the electrical gradient across the cell membrane is dependent on Mg, and it is important in the activity of electrically excitable tissues. Since Mg regulates the movement of calcium in smooth muscle cells, it is also important in peripheral vascular tone and blood pressure. Studies have linked hypo-magnesemia to multiple chronic diseases and to a higher mortality rate. METHODS: To explore trends within our own tertiary care surgical ICU, we sampled our patients' laboratory records in 2001 and in 2011. Hypo-magnesemia in our ICU is defined as an Mg less than 2.0 mg/dL. RESULTS: This retrospective review of all SICU patients from October to December revealed that there was a significant increase (P < 0.01) in the patients with their serum Mg level measured between 2001 (89%) and 2011 (95%). There was a significant decrease (P < 0.001) in patients with hypomagnesemia (< 2 mg/dL) between 2001 (47.5%) and 2011 (33.0%). On the other hand, there was a significant increase (P < 0.001) in patients with normal serum Mg level (> 2 mg/dL) between 2001 (52.5%) and 2011 (67.0%). CONCLUSIONS: There was not only more monitoring of Mg in 2011, but a lower incidence of hypo-Mg compared to 2001. Possible explanations include changing patterns of antibiotic and diuretic use, less amphotericin use, more frequent laboratory surveillance, and better trained ICU practitioners.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".