Estimating serum‐ionized magnesium concentration in hemodialysis patients
Bibliographic record
Abstract
Abstract Introduction Cardiovascular mortality is significantly increased in kidney failure with replacement therapy (KFRT) patients, which is partly mediated by enhanced vascular calcification. Magnesium appears to have anticalcifying capabilities, and hypomagnesemia has been associated with increased mortality in KFRT patients. Ionized magnesium represents the biologically and physiologically active form. As serum ionized magnesium (Mgion) is difficult to assess in clinical routine estimating equations derived from routinely assessed laboratory parameters could facilitate medical treatment. Methods We developed equations to estimate serum Mgion using linear regression analysis in 191 hemodialysis (HD) patients. Reference test was measured ionized magnesium (Mgion). As index tests, we chose estimated Mgion using total magnesium (Mgtot) and other laboratory and demographic variable candidates. Equations were internally validated, using 749 subsequent Mgion measurements. Findings The median patient age was 65 years, 67.5% of the patients were male. Median (interquartile range [IQR]) measured Mgion was 0.64 [0.57, 0.72] mmol/L, 11 (6%) patients were hypo‐ (i.e., <0.45 mmol/L) and 127 (66%) were hypermagnesemic (>0.60 mmol/L). The final equation at the end of the development process included Mgtot, serum ionized, and total calcium concentrations. In the validation dataset, bias (i.e., median difference between measured and estimated Mgion, −0.017 [−0.020, −0.014] mmol/L) and precision (i.e., IQR of bias 0.043 [0.039, 0.047] mmol/L) were small, 90% [88, 93] of estimated values were ±10% of measured values. The equation detected normomagnesemia with overall good diagnostic accuracy (area under the receiver‐operating curve 0.91 [0.89, 0.93]). Discussion Mgion can be estimated from equations containing routinely assessed laboratory variables with high accuracy and good overall performance. These equations might simplify the assessment of ionized magnesium levels in the individual hemodialysis patients and help the treating physician to guide the overall treatment.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".