Effects of pH and the plasma or serum concentrations of total calcium, chloride, magnesium, <scp>l</scp> -lactate, and albumin on the plasma ionized calcium concentration in calves
Bibliographic record
Abstract
Abstract Background The plasma ionized calcium concentration (cCa2+) represents the biologically active form of calcium and is the preferred method for evaluating calcium status in animals. Different pH-corrective equations have been developed for human plasma, but the validity of the equations for bovine plasma is unknown. Hypothesis We hypothesized that pH-corrective equations for bovine plasma would be similar to those used for human plasma; cCa2+ was dependent on the plasma concentrations of total calcium (cTCa), chloride (cCl), L-lactate (cLactate), and albumin (cAlbumin); and the in vitro and in vivo cCa2+-pH relationships would differ. Animals Ten healthy calves (in vitro study), 1426 critically ill calves. Methods The in vitro plasma log10(cCa2+)-pH relationship was determined by CO2 tonometry of 465 plasma samples. Plasma cCl was altered by equivolume dilution of plasma with 3 electrolyte solutions of different cCl. The in vivo plasma cCa2+-pH relationship was investigated and validated using clinicopathologic data extracted from the medical records of 950 (model development) and 476 (model validation) critically ill calves. Results pH-corrective equations for bovine plasma were similar to those used for human plasma. Plasma cCa2+ increased in vitro with increases in plasma cCl. Plasma cCa2+ in critically ill calves was associated with plasma cTCa, blood pH, plasma cCl, serum cMg, and cL-lactate (R2 = 0.69) but not plasma cAlbumin. Conclusions and Clinical Importance Calculation of cCa2+ from cTCa in calf plasma or serum requires adjustment for at least pH and cCl when 1 or both are outside the reference range.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".