The Feline Acute Patient Physiologic and Laboratory Evaluation (Feline APPLE) Score: A Severity of Illness Stratification System for Hospitalized Cats
Bibliographic record
Abstract
BACKGROUND: Scores allowing objective stratification of illness severity are available for dogs and horses, but not cats. Validated illness severity scores facilitate the risk-adjusted analysis of results in clinical research, and also have applications in triage and therapeutic protocols. OBJECTIVE: To develop and validate an accurate, user-friendly score to stratify illness severity in hospitalized cats. ANIMALS: Six hundred cats admitted consecutively to a teaching hospital intensive care unit. METHODS: This observational cohort study enrolled all cats admitted over a 32-month period. Data on interventional, physiological, and biochemical variables were collected over 24 hours after admission. Patient mortality outcome at hospital discharge was recorded. After random division, 450 cats were used for logistic regression model construction, and data from 150 cats for validation. RESULTS: Patient mortality was 25.8%. Five- and 8-variable scores were developed. The 8-variable score contained mentation score, temperature, mean arterial pressure (MAP), lactate, PCV, urea, chloride, and body cavity fluid score. Area under the receiver operator characteristic curve (AUROC) on the construction cohort was 0.91 (95% CI, 0.87-0.94), and 0.88 (95% CI, 0.84-0.96) on the validation cohort. The 5-variable score contained mentation score, temperature, MAP, lactate, and PCV. AUROC on the construction cohort was 0.83 (95% CI, 0.79-0.86), and 0.76 (95% CI, 0.72-0.84) on the validation cohort. CONCLUSIONS AND CLINICAL IMPORTANCE: Two scores are presented enabling allocation of an accurate and user-friendly illness severity measure to hospitalized cats. Scores are calculated from data obtained over the 1st 24 hours after admission, and are diagnosis-independent. The 8-variable score predicts outcome significantly better than does the 5-variable score.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".