Recommendations for the Use of Common Outcome Measures in Pediatric Traumatic Brain Injury Research
Bibliographic record
Abstract
This article addresses the need for age-relevant outcome measures for traumatic brain injury (TBI) research and summarizes the recommendations by the inter-agency Pediatric TBI Outcomes Workgroup. The Pediatric Workgroup's recommendations address primary clinical research objectives including characterizing course of recovery from TBI, prediction of later outcome, measurement of treatment effects, and comparison of outcomes across studies. Consistent with other Common Data Elements (CDE) Workgroups, the Pediatric TBI Outcomes Workgroup adopted the standard three-tier system in its selection of measures. In the first tier, core measures included valid, robust, and widely applicable outcome measures with proven utility in pediatric TBI from each identified domain including academics, adaptive and daily living skills, family and environment, global outcome, health-related quality of life, infant and toddler measures, language and communication, neuropsychological impairment, physical functioning, psychiatric and psychological functioning, recovery of consciousness, social role participation and social competence, social cognition, and TBI-related symptoms. In the second tier, supplemental measures were recommended for consideration in TBI research focusing on specific topics or populations. In the third tier, emerging measures included important instruments currently under development, in the process of validation, or nearing the point of published findings that have significant potential to be superior to measures in the core and supplemental lists and may eventually replace them as evidence for their utility emerges.
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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 | 0.302 | 0.531 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.015 |
| Bibliometrics | 0.025 | 0.021 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".