Predictors of blood ionized calcium concentration in sick adult cattle
Bibliographic record
Abstract
Abstract Background Data on the factors affecting blood ionized calcium concentration (ciCa2+) and diagnostic performance of serum total calcium concentration (ctCa) measurements to detect abnormal blood iCa2+ status are lacking in sick adult cattle. Objective Assess the association of ciCa2+ with venous blood pH, plasma concentrations of chloride (cCl), sodium (cNa), and potassium (cK), and ctCa, and total protein, albumin, and globulin concentrations in sick adult cattle. Animals Two-hundred and sixty-five adult cattle (≥1-year-old) with different diseases. Methods Prospective study. Whole blood pH, ciCa2+, cNa, cK, and cCl were measured using a blood gas and electrolyte analyzer, whereas ctCa, and total protein, and albumin concentrations were determined using an autoanalyzer. The relationship between ciCa2+ and venous blood pH, plasma cCl, cNa, cK, and ctCa, and total protein, albumin, and globulin concentrations was investigated. Sensitivity and specificity were calculated for ctCa for diagnosis of abnormal ciCa2+. Results Sensitivity of ctCa measurements to detect abnormal ciCa2+ was 66.0% whereas specificity of ctCa measurements was 72.3%. Serum total calcium concentration measurements accounted for 42% of adjusted blood ionized calcium (iCa2+7.40) concentration variance. Plasma cCl, and cK had explanatory power of ciCa2+7.40, accounting for an additional 21% and 9% of the variance, respectively. Conclusions and Clinical Importance Serum tCa measurements failed to accurately predict blood iCa2+ status in ill adult cattle. Serum tCa concentrations and plasma cCl were the strongest predictors of ciCa2+ in sick adult cattle.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".