Comment on “Estimating serum‐ionized magnesium concentration in hemodialysis patients”
Bibliographic record
Abstract
We read with interest the recent paper in your journal by Holzmann-Littig and colleagues entitled “Estimating serum-ionized magnesium concentration in hemodialysis patients”.1 The authors concisely and expertly review the importance of measuring ionized magnesium (iMg) in hemodialysis patients, as iMg levels are well known to affect morbidity and mortality in this patient population.2, 3 The importance of measuring iMg is accurately highlighted in the paper, as iMg is the physiologically active component of serum Mg.4 The authors use the direct measurement of iMg using an ion-sensitive electrode as the gold standard reference for iMg concentration to compare with their formula for calculating iMg. In the introduction, the authors state that “…accurate measurement of Mgion is methodologically challenging and cost-intensive in clinical practice.”.1 We believe that this statement is inaccurate and misleading for several reasons. The authors used a Nova Biomedical device, a Nova CRT 8 Electrolyte Analyzer as their reference analyzer for iMg. While this device uses an ion-sensitive electrode and has been shown to be very accurate and precise, it was designed and manufactured over 40 years ago. Although it is a testament to the device that it still functions well, considerable advances in technology have been made in the intervening decades. Today, there are two devices for measuring iMg. One measures iMg along with four other electrolytes in 1 min. The other measures iMg, along with 22 other metabolites, blood gases, and CO-oximetry. Both use a credit-card sized cartridge to house all sensors. They are both cost-effective and not a cost-intensive solution for measuring iMg and are far more accurate and precise than estimating iMg using a formula. The authors are to be commended for the work involved in developing and validating this formula. The authors state that “An equation containing three variables performed well both in terms of accuracy to estimate the ionized value and to predict normomagnesemia.” However, this formula has only been found to be 84% accurate in the external validation cohort, with an area under the curve (AUC) of only 0.78 for determining normomagnesemia.1 Additionally, it has only been studied to predict normomagnesemia, not hypo- or hypermagnesemia, limiting its usefulness in important clinical situations. In our estimation, this falls below an acceptable standard for clinical decision-making and begs the question: “why not just measure ionized magnesium?” Both authors are employees of Nova Biomedical.
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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 | 0.007 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.041 | 0.039 |
| Insufficient payload (model declined to judge) | 0.007 | 0.011 |
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".