Development and validation of a hemangiosarcoma likelihood prediction model in dogs presenting with spontaneous hemoabdomen: The HeLP score
Bibliographic record
Abstract
OBJECTIVE: To calculate a risk prediction model for hemangiosarcoma (HSA) diagnosis in dogs presenting with nontraumatic hemoabdomen. DESIGN: Retrospective multicenter observational cohort study enrolling dogs presented 2003-2016. SETTING: Five academic veterinary medical centers. ANIMALS: A total of 406 dogs with nontraumatic hemoabdomen as the presenting complaint that underwent surgical exploration or necropsy and received a histological diagnosis. Overall, 219 dogs from 3 centers provided the data for model construction, and 187 dogs from 2 centers provided the population for external validation. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The risk score was modeled on 4 predictors: bodyweight (P = 0.01), total plasma protein (P < 0.01), platelet count (P < 0.01), and thoracic radiograph findings (P = 0.02). The incidence of HSA diagnosis was 36%, 76%, and 96% in the low risk (≤40), medium risk (41-55), and high risk (>55) score groups, respectively. The risk score AUROC was 0.85 (95% CI 0.79-0.90) on the construction population, and 0.77 (95% CI 0.70-0.84) on the validation population. CONCLUSIONS: The risk of HSA diagnosis in dogs presenting with nontraumatic hemoabdomen could be predicted using a simple risk score, which could aid in identification and treatment of dogs at lower risk for this diagnosis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".