Corrected Calcium Formula in Routine Clinical Use Does Not Accurately Reflect Ionized Calcium in Hospital Patients
Bibliographic record
Abstract
Background: Adjusting total calcium for serum albumin is a common practice among medical practitioners. Aim: To assess the validity of adjusting total calcium for serum albumin before clinical interpretation in a hospital-based population. Methods: A retrospective analysis involving 678 subjects was performed. Time-matched total calcium, albumin and ionized calcium samples were analyzed. Pearson correlations, intradass correlation coefficients (ICC), and kappa coefficients were used to evaluate agreement between unadjusted total calcium and albumin-adjusted calcium with respect to ionized calcium. Results: For agreement between the Payne albumin-adjusted calcium formula and ionized calcium, ICC was 0.73, R was 0.82 and k was 0.18, and the respective values for unadjusted total calcium were 0.78,0.86 and 0.48. Conclusions: Adjusting total calcium for albumin according to the widely-used Payne formula is associated with predictive properties worse than those of unadjusted total calcium. We recommend using the unadjusted total calcium measurement under normal circumstances; if a precise measure of calcium status is required for clinical management decisions, then ionized calcium should be performed.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".