ABSTRACT 378
Bibliographic record
Abstract
Background and aims: Inattention and hyperactivity problems are over-represented in children with TBI, both pre- and post-injury. These attention difficulties occur across all severities of TBI, suggesting severity of injury alone does not predict individual differences in attention outcomes. Given that frontal lobe integrity is important for attention and that diffuse axonal injury (DAI) in the frontal lobes is common after TBI, we set out to investigate whether frontal lobe DAI predicts attention following TBI. Aims: To establish the trajectory of attention problems in children with TBI as rated by parents and teachers, and to gain an understanding of whether and how acute DAI in the frontal lobes is related to attention ratings. Methods: 58 children/adolescents with mild to severe TBI were enrolled and followed (2009–2013) from PICUs at 5 Canadian children’s hospitals. Institutional Review Boards provided approval of the study. Parents and teachers completed questionnaires assessing ADHD symptoms (Conners Rating Scale – 3rd Edition) at baseline, 3, 6, and 12 months post-injury. Brain MRIs, obtained within first 5 days post-injury, were classified using a novel clinical tool, developed at SickKids. Results: Children with acute frontal DAI showed a pattern of persistently elevated symptoms of inattention and hyperactivity, whereas those without frontal DAI showed initial elevations in post-injury symptoms that recovered to baseline levels. Conclusions: Children with TBI who sustain diffuse axonal injury to the frontal lobes are at risk for persisting attention and hyperactivity difficulties. MRI should be part of routine care to detect DAI with particular attention being paid to injury in the frontal lobe.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.765 | 0.647 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".