82 Detecting Abdominal Visceral Injury in Cases of Suspected Physical Abuse
Bibliographic record
Abstract
Abdominal visceral injury is an uncommon but serious form of physical abuse. Most cases of abusive visceral injury are clinically occult and are at risk of going undiagnosed. Current recommendations suggest screening children with suspicious injuries for occult visceral trauma by measuring serum hepatic transaminases and pancreatic enzymes (primarily lipase) and ordering a CT scan of the abdomen for children with an AST or ALT ≥ 80 mmol/L, a lipase ≥ 100 mmol/L or clinical signs of intra-abdominal injury. This approach was proposed to the Child Maltreatment pediatricians at the 2015 Canadian Symposium on Advanced Practices in Child Maltreatment and was informally adopted by several Canadian Child Maltreatment programs. To review our group’s experience with employing the recommended approach to detecting inflicted visceral injury. A retrospective review of the group’s experience with this approach was completed by the Visceral Trauma Working Group for the October 2018 Symposium. Anonymized data from three centres for the period from January 2016-July 2018 was available for review. In two centres, data on patients who had screening transaminases/lipase and/or abdominal visceral injury was reviewed. In one centre a prospectively collected clinical database was reviewed. 200 children had transaminase +/- lipase measurements. Nine (4.5%) had an AST or ALT ≥ 80 mmol/L. None had a lipase ≥ 100 mmol/L. Ten children had an abdominal CT scan because of elevated transaminases and/or clinical signs/symptoms of blunt torso trauma. Five of the 10 patients had an abdominal visceral injury. No intestinal injuries were identified. Only one child had a visceral injury detected purely as a result of screening blood tests. Physical abuse was suspected as the cause of the visceral injury in 4 of the 5 cases and in all 4, the child was ≤ 5 years of age. Data on suspicious injuries present in screened children was available from one centre. One of 5 children with visceral injury had abdominal bruising and none had chest bruising or greater than 10 suspicious bruises. Following the currently recommended approach in this small group of children resulted in the detection of visceral injury in only one child who did not have other evidence of blunt torso trauma. Given the low incidence of clinically occult visceral injury in this population, a larger study is needed to determine the importance of age, clinical factors and clinically evident injuries when a decision to screen for possible inflicted abdominal visceral injury is being made.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".