Agreement of 2 electrolyte analyzers for identifying electrolyte and acid-base disorders in sick horses
Bibliographic record
Abstract
Abstract Background Use of different analyzers to measure electrolytes in the same horse can lead to different interpretation of acid-base balance when using the simplified strong ion difference (sSID) approach. Objective Investigate the level of agreement between 2 analyzers in determining electrolytes concentrations, sSID variables, and acid-base disorders in sick horses. Animals One hundred twenty-four hospitalized horses. Methods Retrospective study using paired samples. Electrolytes were measured using a Beckman Coulter AU480 Chemistry analyzer (PBMA) and a Nova Biomedical Stat Profile (WBGA), respectively. Calculated sSID variables included strong ion difference, SID4; unmeasured strong ions, USI; and total nonvolatile buffer ion concentration in plasma (Atot). Agreement between analyzers was explored using Passing-Bablok regression and Bland-Altman analysis. Kappa (κ) test evaluated the level of agreement between analyzers in detecting acid-base disorders. Results Methodologic differences were identified in measured Na+ and Cl− and calculated values of SID4 and USI. Mean bias (95% limits of agreement) for Na+, Cl−, SID4, and USI were: −1.2 mmol/L (−9.2 to 6.8), 4.4 mmol/L (−4.4 to 13), −5.4 mmol/L (−13 to 2), and −6.2 mmol/L (−14 to 1.7), respectively. The intraclass correlation coefficient for SID4 and USI was .55 (95%CI: −0.2 to 0.8) and .2 (95%CI: −0.15 to 0.48), respectively. There was a poor agreement between analyzers for detection of SID4 (κ = 0.20, 95%CI, 0.1 to 0.31) or USI abnormalities (κ = −0.04, 95%CI, −0.11 to 0.02). Conclusions and Clinical Importance Differences between analyzer methodology in measuring electrolytes led to a poor agreement between the diagnosis of acid-base disorders in sick horses when using the sSID approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".