Different serum sodium assay, different model for end stage liver disease - sodium scores in patients awaiting liver transplant: A cross-sectional study
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Sodium can be measured with direct or indirect methods; abnormal plasma total protein concentration can impact on sodium measured by indirect ion-selective electrodes (ISE). Serum sodium is an important item to determine the Model for End Stage Liver Disease Sodium (MELD-Na) score, commonly used for liver graft allocation. Patients with cirrhosis usually have hypoproteinemia. The aim of this study was to determine if there was a significant difference between the MELD-Na scores calculated based on the results of two different serum sodium ISE: indirect and direct. METHODS: same date and time) direct and indirect sodium determinations. We calculated the MELD-Na scores with both sodium determinations, and we compared them. RESULTS: There was a significant difference between MELD-Na scores; the mean difference was 0.4±1.3. If MELD-Na score had been determined by the sodium measured by the direct ISE, 69 patients (42%) would have stayed in the same place on the waiting list, 67 patients (40%) would have moved up, and 30 patients (18%) would have moved down. CONCLUSIONS: There was a statistically significant difference between the MELD-Na scores calculated based on the two different sodium concentrations, which would theoretically result in changes in the order of the waiting list. This finding should prompt studies to assess if MELD-Na calculated based on direct methods has a better performance to predict clinically relevant outcomes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".