Participation following pediatric acquired brain injury and some inherent problems with outcome studies
Bibliographic record
Abstract
To my knowledge, the study by Anaby et al.1 is the first to explore the complex phenomenon of participation changes in children and young people following acquired brain injury (ABI), for which the authors are to be complimented. Indeed, participation is increasingly recognized as the fundamental health outcome for all children, including those with ABI, and this subject is under-studied within this patient population. The authors should also be commended in their brain injury study for using valid and reliable tools for children and young people with and without disabilities. Given the Children’s Assessment of Participation and Enjoyment’s strong psychometric properties and demonstrated applicability with children and young people with ABI, perhaps this tool could have been included in the recently published recommendations for the use of common outcome measures in pediatric traumatic brain injury research.2 Several aspects of this paper by Anaby et al. are interesting. First, their results clearly highlight that even following a mild ABI injury, participation patterns are restricted, and they show for the first time how participation changes over time after a brain injury. Indeed, a non-mild (i.e. moderate/severe) ABI in children and young people can influence long-term changes in participation outcome in leisure activities. The study adds further support to the notion that length of stay and cause of injury are not predictors of this outcome. As such, one could argue that future pediatric brain injury outcome studies need not consider these data and that more energy could be put towards collecting, by interdisciplinary clinical teams, important data for use in traumatic brain injury outcome studies, such as Glasgow Coma Scale (GCS) scores. It is surprising even today, that there is still such a high percentage of medical dossiers missing GCS scores, when it is evident that this injury severity measure constitutes basic information essential to important pediatric brain injury research. Like in other studies, excluding potential participants from their sample due to missing GCS scores may have created a selection bias, a limitation of all cohort studies. Despite this limitation, the authors are commended for their ability to conduct a prospective longitudinal cohort study with three assessment times with such a large number of children and young people with ABI. The study results also demonstrated how family characteristics influence some aspects of participation. Participation in recreational activities changed as a function of socio-economic status of families of these children and young people, and family function influenced change in only skill-based activities that are formal and organized in nature. These results add new knowledge about participation following pediatric brain injury. I agree with the authors that ‘family characteristics … may be more amenable to change through intervention in comparison to the severity of injury or any other medical variable’, but I would have appreciated the authors’ insight regarding concrete examples of how this could be done. Last, most brain injury outcome studies typically use ‘time/date of injury’ as a baseline to facilitate comparisons among outcome studies. Having chosen when children returned to school as the baseline, the study by Anaby et al. thus limits comparisons with other research. I agree with the authors that this is ‘a crucial transition time’, but ‘going to school’ is an important participation outcome in itself and we know not all children return to school after sustaining an ABI or traumatic brain injury. Moreover, in a perfect world, it would have been ideal to have compared the participants’ baseline scores (at return to school) with those in the last 4 months prior to their injury. This additional information might also be helpful when planning the ‘return’ to leisure activities practiced prior to the injury3,4 and to injury prevention efforts in general. Examining the changes in levels of participation observed during the first year of recovery after brain injury is an exciting area of research. Indeed these changes are influenced by child factors (injury severity) and family factors (socio-economic status and function). It is hoped that the study by Anaby et al. encourages others to pursue research into improving our understanding of the phenomenon of participation post pediatric 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 | 0.330 | 0.529 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".