Mechanism of Injury and Clinical Recovery Outcomes Following Pediatric Concussion
Bibliographic record
Abstract
Children with concussion are injured through a variety of mechanisms, but the relationship between mechanism of injury (MOI) and recovery outcomes is unclear due to small sample sizes and varied methodological designs. Our objective was to examine the association of MOI and clinical recovery in youth with concussion using a large dataset collated from a single, multisite study. We hypothesized that sport-related concussion would be related to better clinical presentation and faster recovery trajectories compared to other mechanisms of concussion. This study was a secondary analysis of data collected during the Predicting and Preventing Postconcussive Problems in Pediatrics study. Children and adolescents with concussion ( n = 3056) completed the Child Sport Concussion Assessment Tool 3rd Edition and Postconcussion Symptom Inventory (PCSI) within 48 h following injury. Follow-up sessions at 1-, 2-, 4-, 8-, and 12-weeks post injury were completed using the PCSI and Pediatric Quality of Life Scale (PedsQL) scales. Acute clinical outcomes were analyzed using analysis of variances or chi-square analyses, while recovery trajectories were evaluated using linear and logistic regression. No MOI-based differences in acute clinical presentation were observed, except for balance outcomes in 13–17 year old ( F [2,1001] = 5.69, p = 0.003). Symptoms improved over time regardless of age ( p < 0.05). In 8–12 and 3–17 year olds, quality of life improved over time and was significantly higher in the sports group ( p < 0.05). The “other” mechanism group had higher odds of persistent symptoms at 4-week than the sports group in 8–12 year olds (OR = 2.01, 95% CI = 1.20, 3.40, p = 0.008), while this finding was reversed in the 13–17 group (OR = 0.61, 95% CI = 0.38, 0.99, p = 0.045). Sport-related concussions were generally associated with better symptom and quality of life scores in older children, but these differences were modest and unlikely to be clinically significant. Regardless of MOI, most children experienced clinical improvements across the first three months following concussion.
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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 | 0.001 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".