The Simple Pediatric Activity Ultrasound Score (SPAUSS) for the Accurate Detection of Pediatric Inflammatory Bowel Disease
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to identify the most significant ultrasound (US) parameters that predict inflammatory activity and develop a simple US activity score. METHODS: Patients were identified through retrospective evaluation of an established database of children with inflammatory bowel disease (IBD). Patients with endoscopy and US within 60 days were included (N = 75). US parameters evaluated included: bowel wall thickness (BWT), mesenteric inflammatory fat, lymphadenopathy, and hyperemia. The weighted kappa statistic was calculated to assess agreement between sonographic and endoscopically identified disease location. Using a proportional odds model and ordinal logistic regression, statistically significant (P < 0.05) parameters were used to generate a score. Variables were weighted to classify individuals into severity classes. Receiver operating characteristic curves were plotted to demonstrate the score's discriminative and predictive capacity. RESULTS: There was substantial agreement between US and endoscopy for all disease locations (weighted kappa = 0.85) and substantial agreement for ileocolonic disease (weighted kappa = 0.96). Two sonographic parameters were identified as contributing significantly to disease activity: BWT and mesenteric inflammatory fat (P < 0.05). A predictive score was developed incorporating BWT, hyperemia and inflammatory fat, and receiver operating characteristic curve curves demonstrated good predictive capacity to distinguish between the absence of disease (normal) and active disease with an area under the curve of 82.1%. CONCLUSIONS: The most important sonographic parameters for predicting disease activity were BWT and mesenteric inflammatory fat. When combined with hyperemia into a simple score, there was accurate detection of inflammatory activity in children with inflammatory bowel disease. This score may facilitate noninvasive, bedside detection of inflammation, and standardize the use of US in children.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".