A formula to predict corrected calcium in haemodialysis patients
Bibliographic record
Abstract
BACKGROUND: The conventional calcium correction formula (corrected total calcium (mmol/L) = TCa (mmol/L) + 0.02 [40 (g/L) - albumin (g/L)]) is broadly applied for the estimation of serum calcium in haemodialysis (HD) patients, despite the fact that it was not derived or validated in a HD population. A novel formula was derived and validated for corrected serum calcium in HD patients. METHODS: Total calcium (TCa), ionized calcium (iCa(2+)), magnesium, phosphate, albumin and bicarbonate were collected from 60 HD patients to derive the formula. A validation set of 237 stable HD patients was then examined, and subjects were classified as hyper-, hypo- and normocalcaemic based on the iCa(2+). Agreement of the new formula was calculated with iCa(2+) as the gold standard, using the intraclass correlation coefficient (ICC). This was compared to the agreement between iCa(2+) and the following: uncorrected total serum calcium (TCa), the conventional correction formula, the Orrell formula and the Clase formula. RESULTS: Using multiple linear regression the following formula was derived: corrected total calcium (mmol/L) = TCa (mmol/L) + 0.01 [30 (g/L) - albumin (g/L)]. The new formula had superior agreement compared to all of the other formulae. There was a statistically significant greater agreement between the new formula and the iCa(2+) as compared to the conventional formula (P < 0.01). However, the new formula did not significantly outperform the Orrell formula, the Clase formula or Total calcium. CONCLUSIONS: The use of our simple new formula should enable more appropriate decision making compared to the conventional formula in the highly complex HD population.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".