Predicting Wellness After Pediatric Concussion
Bibliographic record
Abstract
OBJECTIVE: Concussion in children and adolescents is a prevalent problem with implications for subsequent physical, cognitive, behavioral, and psychological functioning, as well as quality of life. While these consequences warrant attention, most concussed children recover well. This study aimed to determine what pre-injury, demographic, and injury-related factors are associated with optimal outcome ("wellness") after pediatric concussion. METHOD: A total of 311 children 6-18 years of age with concussion participated in a longitudinal, prospective cohort study. Pre-morbid conditions and acute injury variables, including post-concussive symptoms (PCS) and cognitive screening (Standardized Assessment of Concussion, SAC), were collected in the emergency department, and a neuropsychological assessment was performed at 4 and 12 weeks post-injury. Wellness, defined by the absence of PCS and cognitive inefficiency and the presence of good quality of life, was the main outcome. Stepwise logistic regression was performed using 19 predictor variables. RESULTS: 41.5% and 52.2% of participants were classified as being well at 4 and 12 weeks post-injury, respectively. The final model indicated that children who were younger, who sustained sports/recreational injuries (vs. other types), who did not have a history of developmental problems, and who had better acute working memory (SAC concentration score) were significantly more likely to be well. CONCLUSIONS: Determining the variables associated with wellness after pediatric concussion has the potential to clarify which children are likely to show optimal recovery. Future work focusing on wellness and concussion should include appropriate control groups and document more extensively pre-injury and injury-related factors that could additionally contribute to wellness. (JINS, 2019, 25, 375-389).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".