Splenic Rupture in Children With Portal Hypertension
Bibliographic record
Abstract
INTRODUCTION: Massive splenomegaly from portal hypertension (PHTN) in children raises the specter of splenic rupture; however, the incidence, etiology, and risk of rupture have not been studied, nor have existing practices to reduce risk. We therefore performed an international survey to describe the splenic rupture cases in PHTN and to describe the existing empirical practice among hepatologists. METHODS: A questionnaire was constructed to elicit cases of splenic rupture and collect hepatologists' common practices for prevention of splenic rupture. Pediatric hepatologists working in selected tertiary academic centers in the United States, Canada, and the United Kingdom were contacted. RESULTS: Hepatologists from 30 of 35 centers who met the inclusion criteria replied to the survey. Thirteen cases of splenic rupture were described of which 11 resulted from trauma. In the opinion of the practitioners, high-risk activities were football, hockey, and wrestling. Sixty-one percent recommended total restriction from high-risk activities. Seventy-four percent stated that platelet count had no effect on this decision and 61% advised a spleen guard for certain activities. CONCLUSIONS: Splenic rupture in patients with PHTN and splenomegaly seems to be rare. The reported splenic rupture cases were mostly related to falling (and not to participation in sports). There was general agreement among hepatologists about restricting high impact sports. There was variation in recommendations regarding the use of a spleen guard. The authors recommend use of spleen guards in children with splenomegaly from PHTN for physical activities with risk of fall or blunt abdominal trauma.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".