Acid-base disorders in sick goats and their association with mortality: A simplified strong ion difference approach
Bibliographic record
Abstract
Abstract Objectives To investigate the acid-base status of sick goats using the simplified strong ion difference (sSID) approach, to establish the quantitative contribution of sSID variables to changes in blood pH and HCO3− and to determine whether clinical, acid-base, and biochemical variables on admission are associated with the mortality of sick goats. Animals One hundred forty-three sick goats. Methods Retrospective study. Calculated sSID variables included SID using 6 electrolytes unmeasured strong ions (USI) and the total nonvolatile buffer ion concentration in plasma (Atot). The relationship between measured blood pH and HCO3−, and the sSID variables was examined using forward stepwise linear regression. Cox proportional hazard models were constructed to assess associations between potential predictor variables and mortality of goats during hospitalization. Results Hypocapnia, hypokalemia, hyperchloremia, hyperlactatemia, and hyperproteinemia were common abnormalities identified in sick goats. Respiratory alkalosis, strong ion acidosis, and Atot acidosis were acid-base disorders frequently encountered in sick goats. In sick goats, the sSID variables explained 97% and 100% of the changes in blood pH and HCO3−, respectively. The results indicated that changes in the respiratory rate (<16 respirations per minute), USI, and pH at admission were associated with increased hazard of hospital mortality in sick goats. Conclusions and Clinical Importance The sSID approach is a useful methodology to quantify acid-base disorders in goats and to determine the mechanisms of their development. Clinicians should consider calculation of USI in sick goats as part of the battery of information required to establish prognosis.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".