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 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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".