P.122 Restricted diffusion of white matter in infants with subdural hematoma
Bibliographic record
Abstract
Background: Inflicted head injury is a major cause of infant morbidity and mortality. The extent of traumatic brain injury in infants is often best characterized by diffusion weighted magnetic resonance imaging. In this cases series we describe four infants aged 6-19 months, with small unilateral subdural hematomas secondary to abusive head trauma accompanied by extensive areas of restricted diffusion weighted imaging isolated to the cerebral white matter. Methods: Retrospective, single-centre case series of four children with small unilateral subdural hematomas with early and delayed MR imaging with diffusion weighted imaging. Results: In three cases there was acute diffusion restriction ispilateral to the subdural, while in one case diffusion restriction was present bilaterally. All patients had multiple seizures and bilateral multilayered retinal hemorrhages. After non-surgical treatment, all patients survived albeit with significant motor and cognitive deficits and significant cortical atrophy on long-term followup imaging. Conclusions: These four cases highlight that relatively small subdural hematomas following child abuse can manifest with extensive white matter injury only evident at early stages with diffusion weighted imaging. We propose that selective white matter injury as a result of either reperfusion or axonal degeneration in response to the initial insult accounts for this novel pattern of infantile traumatic brain injury.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".