Validation of a point-of-care handheld blood total calcium analyzer in postpartum dairy cows
Bibliographic record
Abstract
, TaiDoc, New Taipei, Taiwan) to estimate circulating Ca concentrations in postpartum dairy cows. Whole blood was collected from 251 multiparous cows between 1 and 4 d in milk from 2 commercial dairy herds in Ontario, Canada. Blood total calcium concentration (tCa) was analyzed in whole blood, fresh plasma, and thawed plasma, and compared with tCa results from thawed serum analyzed in a diagnostic laboratory (using a Cobas Calcium Gen 2 kit, Roche Diagnostics, Indianapolis, IN) as the reference test (RT). Lin's concordance correlation coefficient (βrho;) and Bland-Altman (B-A) plots were assessed to evaluate the agreement between the RT and CM results in each type of sample. Receiver operating characteristic curve analyses were used to describe the accuracy of each test against the categorized RT results (at a cut-point of ≤2.14 mmol/L). Samples where the meter gave a nonquantitative result ("high" or "low"; thawed plasma: 3/247; fresh plasma: 6/100; and whole blood: 20/98) were not included in the βrho; and B-A analyses. Lin's correlation coefficients demonstrated poor agreement between tests (thawed plasma: βrho; = 0.16; fresh plasma: βrho; = 0.21; and whole blood: βrho; = 0.23). Fresh plasma (using a cut-point of 2.55 mmol/L as measured on the CM) had the greatest diagnostic sensitivity (72%), specificity (86%), and accuracy (77%) for determining subclinical hypocalcemia, but that would still misclassify 23% of samples. In addition to substantial variability, the B-A plots revealed bias with changing concentrations of calcium. Because of low sensitivity on whole blood (58%) or thawed plasma (56%), measurement with the CM is not recommended on these types of samples. This rapid and low-cost meter was not sufficiently accurate to quantify blood Ca concentration, but when used with fresh plasma it might be useful as a screening tool for subclinical hypocalcemia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".