Association of the<i>APOE-ε4</i>allele with outcome of traumatic brain injury in children and youth: a meta-analysis and meta-regression
Bibliographic record
Abstract
OBJECTIVE: To disentangle the temporal relationship between the APOE-ε4 allele and outcomes of paediatric traumatic brain injury (TBI). METHODS: PubMed, EMBASE, Web of Science, MEDLINE, PsychINFO and HuGE Navigator Genopedia databases were searched from their inception up to January 2015 without language limitations. Included studies were analysed under a dominant genetic model to assess the association between the APOE-ε4 allele and poor outcomes of paediatric TBI at 6 months. Meta-regression was used to assess trends over time. RESULTS: Of the 325 initially identified records, 6 studies were selected and analysed based on inclusion/exclusion criteria. A total of 358 cases of paediatric TBI were included. 2 studies assessed outcomes at multiple time points ranging from 3 to 36 months; 4 studies assessed outcomes at a single time point (either 6 or 12 months). At 6 months, there is 2.36 (95% CI 1.26 to 4.42; p=0.007) times higher odds of poor outcome following TBI in children with at least one APOE-ε4 allele, compared with the children without. Further, the adjusted odds suggested an increasing trend of 7% per month (95% CI -9 to 25; p=0.359). CONCLUSIONS: This meta-analysis provides cumulative evidence that the APOE-ε4 allele is important to the prognosis of paediatric TBI, but may have a different effect compared with adult TBI; moreover, this effect may be time dependent.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.059 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".