Contribution of Unmeasured Anions to Acid–Base Disorders and its Association with Altered Demeanor in 264 Calves with Neonatal Diarrhea
Bibliographic record
Abstract
BACKGROUND: The quantitative effect of strong electrolytes, unmeasured anions (UAs), pCO2 , and plasma protein concentrations in determining plasma pH and bicarbonate (HCO3 (-) ) can be demonstrated using the physicochemical approach. Demeanor of calves with diarrhea is associated with acidemia, dehydration, and hyper-d-lactatemia. HYPOTHESIS: Unmeasured anions are a major factor influencing changes in plasma pH and HCO3 (-) of calves with diarrhea and UAs and strong UAs, estimated by anion gap (AG) and strong ion gap (SIG), respectively, are more strongly associated with alteration of demeanor compared to other acid-base variables. ANIMALS: A total of 264 calves with diarrhea from two data sets (DS1 and DS2). METHODS: Retrospective study. Forward stepwise regression was performed to determine the relationship between measured pH or HCO3 (-) , and physicochemical variables. A two-way ANOVA was performed to investigate the association between acid-base variables and attitude (bright, obtunded, and stuporous), posture (standing, sternal or lateral recumbency), and strength of suckling reflex (strong, weak, or absent). RESULTS: Increased strong UAs estimated by SIG was the most important contributor to changes in measured pH and HCO3 (-) (DS1: r(2) 66 and 59%, DS2: 39 and 42%, P < .0001). SIG and AG were correlated to deteriorating calf demeanor for all three clinical scoring categories: attitude, posture, and suckle reflex (P < .0001). CONCLUSION AND CLINICAL RELEVANCE: Elevated concentrations of strong UAs were the primary cause of acidemia and had an important influence on the demeanor of calves with diarrhea. These findings emphasize the importance of the calculation of UAs when evaluating acid-base abnormalities in calves.
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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 | 0.000 | 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".