Head Trauma and Intracranial Hemorrhage in Children With Idiopathic Thrombocytopenic Purpura
Bibliographic record
Abstract
BACKGROUND: The current guidelines for management of idiopathic thrombocytopenic purpura (ITP) does not address head trauma and the current emergency pediatric head trauma management guidelines do not address children with ITP. The characteristics of patients who develop intracranial hemorrhage (ICH) as a result of head trauma or the management of head trauma in patients with ITP are not clear. OBJECTIVES: Review the literature to identify and describe the characteristics and outcomes of intracranial haemorrhage as a result of head trauma in children with ITP. METHODS: We reviewed literature using Medline, Embase, and PUBMED databases from inception until December 2013. Articles were included if they described patients with head trauma and intracranial bleeding in children with ITP. Nine relevant articles met inclusion criteria and were included. Three case reports, 3 institution survey studies, and 5 retrospective chart reviews. RESULTS: There were 114 cases of ICH reported in children with ITP, and 26% (n = 30) were identified to have ICH due to head trauma. Of the 30 children with ITP who had an ICH in the context of head injury, 23% (7 patients) died as a result of ICH and 13% (4 patients) suffered significant neurological sequelae. Twenty-seven percent were 3 years or younger when the age was reported CONCLUSIONS: Intracranial haemorrhage after head trauma in children with ITP leads to significant morbidity and mortality. As such, more thorough investigations, including radiological imaging and aggressive treatment, are recommended for children with ITP presenting with head injuries.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".