Day of injury CT and late MRI findings: Cognitive outcome in a paediatric sample with complicated mild traumatic brain injury
Bibliographic record
Abstract
OBJECTIVES: Complicated mild traumatic brain injury (mTBI) or cmTBI is based on the presence of visibly identifiable brain pathology on the day-of-injury computed tomography (CT) scan. In a paediatric sample the relation of DOI CT to late MRI findings and neuropsychological outcome was examined. METHODS: MRI (>12 months) was obtained in paediatric cmTBI patients and a sample of orthopaedically injured (OI) children. Those children with positive imaging findings (MRI+) were quantitatively compared to those without (MRI-) or with the OI sample. Groups were also compared in neurocognitive outcome from WASI sub-tests and the WISC-IV Processing Speed Index (PSI), along with the Test of Everyday Attention for Children (TEA-Ch) and a parent-rated behavioural functioning measure (ABAS-II). RESULTS: Despite the MRI+ group having significantly more DOI CT findings than the MRI- group, no quantitative differences were found. WASI Vocabulary and Matrix Reasoning scores were significantly lower, but not PSI, TEA-Ch or ABAS-II scores. MRI+ and MRI- groups did not differ on these measures. CONCLUSIONS: Heterogeneity in the occurrence of MRI-identified focal pathology was not associated with uniform changes in quantitative analyses of brain structure in cmTBI. Increased number of DOI CT abnormalities was associated with lowered neuropsychological performance.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".