Corrected Calcium Formula in Routine Clinical Use Does Not Accurately Reflect Ionized Calcium in Hospital Patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it