Physicochemical Interpretation of Acid-Base Abnormalities in 54 Adult Horses with Acute Severe Colitis and Diarrhea
Bibliographic record
Abstract
BACKGROUND: The quantitative effect of strong electrolytes, pCO2 , and plasma protein concentration in determining plasma pH and bicarbonate concentrations can be demonstrated with the physicochemical approach. Plasma anion gap (AG) and strong ion gap (SIG) are used to assess the presence or absence of unmeasured anions. HYPOTHESES: The physicochemical approach is useful for detection and explanation of acid-base disorders in horses with colitis. AG and SIG accurately predict hyperlactatemia in horses with colitis. ANIMALS: Fifty-four horses with acute colitis and diarrhea. METHODS: Retrospective study. Physicochemical variables were calculated for each patient. ROC curves were generated to analyze sensitivity and specificity of AG and SIG for predicting hyperlactatemia. RESULTS: Physicochemical interpretation of acid-base events indicated that strong ion metabolic acidosis was present in 39 (72%) horses. Mixed strong ion acidosis and decreased weak acid (hypoproteinemia) alkalosis was concomitantly present in 17 (30%) patients. The sensitivity and specificity of AG and SIG to predict hyperlactatemia (L-lactate > 5 mEq/L) were 100% (95% CI, 66.4-100; P < .0001) and 84.4% (95% CI, 70.5-93.5 P < .0001). Area under the ROC curve for AG and SIG for predicting hyperlactatemia was 0.95 (95% CI, 0.86-0.99) and 0.93 (95% CI, 0.83-0.99), respectively. CONCLUSION AND CLINICAL RELEVANCE: These results emphasize the importance of strong ions and proteins in the maintenance of the acid-base equilibria. AG and SIG were considered good predictors of clinically relevant hyperlactatemia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".