Intra-individual variability in recovery from paediatric acquired brain injury: Relationship to outcomes at 1 year
Bibliographic record
Abstract
OBJECTIVE: To determine the relationship between the amount of intra-individual variability in measures of abilities and participation throughout the first 8 months of recovery from ABI and outcome scores at 1 year. Greater amounts of intra-individual variability throughout recovery are hypothesized to predict better outcome scores at 1 year. RESEARCH DESIGN: This is a secondary data analysis of a longitudinal cohort study. METHODS: Eighty-seven children and youths were assessed with self and proxy report measures of child functioning, family functioning and environmental factors at regular intervals after ABI. Mixed-effects modelling was used to determine individual linear recovery trajectories. Intra-individual variability was defined as the intra-individual standard deviation of the residuals around the recovery line. RESULTS: Less intra-individual variability in recovery predicts better outcomes of physical health (Child Health Questionnaire), behavioural functioning (Strengths and Difficulties Questionnaire), family coping (Impact on Family Scale) and impact of environmental barriers (Craig Hospital Inventory of Environmental Factors). As amount of intra-individual variability increases, outcomes become poorer. CONCLUSIONS: Findings support the existence of intra-individual variability in instrument scores over time in this sample and the impact of this variability on several outcomes at 1 year. Potential clinical and research implications are discussed.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".