Comparación de dos modelos para evaluar el estado acido-base de caballos con colitis
Bibliographic record
Abstract
Este estudio comparó los modelos Henderson- Hasselbalch (H-H) y Diferencia de Iones Fuertes (DIF+) para evaluar las alteraciones acido-base en caballos con colitis. Además, evaluó el valor predictivo de las pruebas subrogadas para detectar hiperlactatemia. Las historias clínicas de 31 caballos presentados al Ontario Veterinary College Teaching Hospital durante los años 2009 y 2010 fueron revisadas. Acidosis metabólica fue el desorden acido-base más común. El modelo de H-H diagnosticó acidosis metabólica en 61% de los caballos, mientras el modelo de DIF+ detecto un 87%. El análisis de curvas ROC reveló que el strong ion gap y el anión gap corregido tienen mayor sensibilidad y valor predictivo negativo que el anión gap. En conclusión, el modelo de DIF+ y DIF+ simplificado ofrecen mayores ventajas en la evaluación del estado acido- base en caballos con colitis. / Abstract. This study compared the approach the performance of the approach of the Henderson- Hasselbalch (H-H) and Strong Ion Difference (SID+) in analysis of acid base imbalances in horse with colitis. Additionally, to investigate the relationship between serum L-lactate and subrogates tests for hyperlacatemia. Medical records of 31 horses presented to the Ontario Veterinary College Teaching Hospital from 2009 to 2010 were reviewed. Metabolic acidosis was the most common acid-base disorder. Metabolic acidosis was detected in 61% of horse with the H-H approach, while SID+ 87 % were detected by the SID+ approach. The ROC curve analysis revealed that Strong Ion Gap and corrected Anion Gap are more reliable predictors of hyperlacatemia than Anion Gap. In conclusion, SID+ and simplified SIG approach is a more sensitive system to detect acid base imbalances in horses with colitis.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".