Advancing Concussion Assessment in Pediatrics (A-CAP): a prospective, concurrent cohort, longitudinal study of mild traumatic brain injury in children: study protocol
Bibliographic record
Abstract
INTRODUCTION: Paediatric mild traumatic brain injury (mTBI) is a public health burden. Clinicians urgently need evidence-based guidance to manage mTBI, but gold standards for diagnosing and predicting the outcomes of mTBI are lacking. The objective of the Advancing Concussion Assessment in Pediatrics (A-CAP) study is to assess a broad pool of neurobiological and psychosocial markers to examine associations with postinjury outcomes in a large sample of children with either mTBI or orthopaedic injury (OI), with the goal of improving the diagnosis and prognostication of outcomes of paediatric mTBI. METHODS AND ANALYSIS: A-CAP is a prospective, longitudinal cohort study of children aged 8.00-16.99 years with either mTBI or OI, recruited during acute emergency department (ED) visits at five sites from the Pediatric Emergency Research Canada network. Injury information is collected in the ED; follow-up assessments at 10 days and 3 and 6 months postinjury measure a variety of neurobiological and psychosocial markers, covariates/confounders and outcomes. Weekly postconcussive symptom ratings are obtained electronically. Recruitment began in September 2016 and will occur for approximately 24 months. Analyses will test the major hypotheses that neurobiological and psychosocial markers can: (1) differentiate mTBI from OI and (2) predict outcomes of mTBI. Models initially will focus within domains (eg, genes, imaging biomarkers, psychosocial markers), followed by multivariable modelling across domains. The planned sample size (700 mTBI, 300 OI) provides adequate statistical power and allows for internal cross-validation of some analyses. ETHICS AND DISSEMINATION: The ethics boards at all participating institutions have approved the study and all participants and their parents will provide informed consent or assent. Dissemination will follow an integrated knowledge translation plan, with study findings presented at scientific conferences and in multiple manuscripts in peer-reviewed journals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".