The multivariate predictive model to estimate ionized calcium concentration from serum biochemical results in dogs: External validation
Bibliographic record
Abstract
BACKGROUND: Predicted ionized calcium (piCa) can be calculated from routine biochemistry variables using a recently developed predictive model in dogs. However, it has not been evaluated with variables measured from multiple laboratories. OBJECTIVES: We aimed to (a) externally validate piCa in dogs where biochemistry results were obtained from different analyzers, and (b) compare the diagnostic performances of piCa and total calcium (tCa). METHODS: A cross-sectional multicentric study on 138 dogs from three different hospitals was performed. The sensitivity (Sen), specificity (Spe), positive (PPV) and negative predictive values (NPV), and diagnostic discordance of piCa and tCa were calculated using logistic regression for ionized hypercalcemia and hypocalcemia. Diagnostic performance fluctuations across hospitals were also assessed. RESULTS: For ionized hypercalcemia, the Sen (81.8%), Spe (96.1%), PPV (69.2%), NPV (97.7%), and diagnostic discordance (5.1%) of piCa were not significantly different among hospitals or from those of tCa. For ionized hypocalcemia, the Sen (range: 9.7%-53.8%) and Spe (range: 95.6%-99.6%) of piCa and tCa (Sen range: 16.2%-87.8%; Spe range: 58.3%-98.1%) varied across hospitals, although to a lesser extent for piCa. The diagnostic discordances of piCa (20.3%) and tCa (25.4%) were close. The prediction interval (PI) of piCa demonstrated high Sen to screen for ionized hypercalcemia (100%) and hypocalcemia (range: 75%-93.3%), and high Spe to diagnose ionized hypercalcemia and hypocalcemia (100% for both). CONCLUSIONS: These results support the external validation of piCa in dogs. Its PI represents a notable advantage over tCa to help clinicians explore calcium-related disorders when ionized calcium cannot be readily 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 | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".