Physicochemical Approach to Determine the Mechanism for Acid–Base Disorders in 793 Hospitalized Foals
Bibliographic record
Abstract
BACKGROUND: The quantitative effect of strong electrolytes, unmeasured strong anions (UAs), pCO2, and plasma protein concentrations in determining plasma pH can be demonstrated using the physicochemical approach. Plasma anion gap (AG) and strong ion gap (SIG) are used to assess UAs in different species. HYPOTHESES: Strong ions are a major factor influencing changes in plasma pH of hospitalized foals. AG and SIG accurately predict severe hyper-L-lactatemia ([L-lac(-)] > 7 mmol/L). ANIMALS: Seven hundred and ninety three hospitalized foals < 7 days old. METHODS: Retrospective study. The relationship between measured pH and physicochemical variables, and the relationship between plasma [L-lac(-)] and AG and SIG, were determined using regression analyses. Optimal AG and SIG cut points to predict hyper-L-lactatemia were identified using an ROC curve analysis. RESULTS: Combined, the measured strong ion difference and SIG accounted for 54-69% of the changes in the measured arterial pH of hospitalized foals. AG and SIG were significantly associated with plasma [L-lac(-)] (P < .0001). The receiver operator characteristics (ROC) AUC of AG and SIG for prediction of severe hyper-L-lactatemia were 0.89 (95%CI, 0.8-0.95; P < .0001) and 0.90 (95%CI, 0.81-0.96; P < .0001), respectively. Severe hyper-L-lactatemia was best predicted by AG > 27 mmol/L (sensitivity 80%, 95%CI, 56-94, specificity 85%, 95%CI, 73-93; P < .0001) and SIG <-15 mmol/L (sensitivity 90%, 95%CI, 68-98; specificity 80%; 95%CI, 68-90; P < .0001). CONCLUSION AND CLINICAL RELEVANCE: Altered concentrations of strong ions (Na(+), K(+), Cl(-)) and UAs were the primary cause of acidemia of hospitalized foals. AG and SIG were good predictors of hyper-L-lactatemia and could be used as surrogate tests.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".