Albumin‐corrected calcium and ionized calcium in stable haemodialysis patients
Bibliographic record
Abstract
BACKGROUND: It is ionized calcium that is physiologically active and under homeostatic control; however, total calcium is more conveniently measured. Formulae for correction of calcium to account for albumin binding have not been validated in a dialysis setting. METHODS: We measured ionized calcium simultaneously with total calcium (t[Ca]), albumin, total protein and pH before dialysis in 50 stable outpatients and convalescent inpatients. RESULTS: Although 92% of patients were taking calcium supplements and 70% taking alphacalcidol, 11 patients (22%) had ionized hypocalcaemia. To facilitate comparison of calculated ionized calcium, measured total calcium (t[Ca]), and 'corrected' calcium (c[Ca]), with the criterion measure of ionized calcium, all measurements were converted to z scores, standardized on the normal range for each variable. Results are expressed as intraclass correlation coefficients (ICC: 0, all differences are due to error; 1, all differences are due to between patient variation). CONCLUSIONS: None of the published formulae greatly improved the test characteristics beyond simply using the total calcium. A correction formula in widespread use (Payne), quoted in reference texts, agreed less well with ionized calcium than did the unadjusted measured calcium. Correction formulae should be abandoned in favour of the use of uncorrected calcium. In cases of doubt, ionized calcium should be directly measured.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".