Adjusting copper concentrations for caeruloplasmin levels in routine clinical practice
Bibliographic record
Abstract
BACKGROUND: An investigation on copper metabolism usually includes the measurement of serum levels of copper and caeruloplasmin. Using these levels, some laboratories derive levels of non-caeruloplasmin-bound copper (NCC); however, a considerable number of patients may show negative values, which is not physiologically possible. AIM: To derive an equation for adjusted copper in a manner similar to that widely accepted for adjusted calcium. METHODS: A linear regression equation for the relationship between caeruloplasmin and copper was used: [copper] (micromol/l) = 0.052x[caeruloplasmin] (mg/l). An equation for copper adjusted for caeruloplasmin was derived using this equation and the reference interval of 10-25 micromol/l for copper. RESULTS: The derived equation was [adjusted copper] (micromol/l) = [total copper] (micromol/l)+0.052x[caeruloplasmin] (mg/l)+17.5 (micromol/l). The adjusted copper concentrations on the 2.5th and 97.5th centiles were 12.7 and 21.5 micromol/l, respectively, with the population having a gaussian distribution. The relationship between NCC and the adjusted copper concentrations is linear and independent of caeruloplasmin concentration. CONCLUSION: Calculation of copper adjusted for caeruloplasmin uses the same variables as those for NCC. Accordingly, the problems that are caused by the lack of specificity of caeruloplasmin immunoassays are the same as those identified for NCC. This calculation, however, overcomes the negative values that are found in a considerable minority of patients with NCC, as well as age and sex differences in the caeruloplasmin reference interval. As the concept is already familiar to non-laboratory healthcare professionals in the form of calcium adjusted for albumin, this method is potentially less confusing than that for NCC.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".