Pediatric concussion working memory outcomes: a scoping review
Bibliographic record
Abstract
Objective: Characterize the working memory (WM) profile of children and youth who have experienced concussion by systematically synthesizing existing literature on the neuropsychological outcomes of these injuries.Methods: Implemented a peer-reviewed search strategy combining key concepts of concussion/mild traumatic brain injury (mTBI), WM, and pediatrics across MedLine, Embase, PsycINFO, and CINAHL. Included studies written in English with extractable results on a WM outcome measure in individuals aged 21 and under who experienced concussion. Applied narrative synthesis to identify trends in the literature. Assessed risk of bias and quality using the NHLBI’s Quality Assessment of Observational Cohort and Cross-Sectional Studies.Results: 40 articles met inclusion criteria. 34/40 studies compared WM performance in children or youth with concussion to healthy controls, pre-injury performance, or normative values, of which 15 reported significantly lower WM performance in the concussion sample. Visual/spatial WM was more consistently impacted than verbal WM. Cognitive demanding dual-task conditions were also reliably impacted.Conclusion: Literature indicated that WM is vulnerable to negative outcomes following pediatric concussion, yet the nature of outcomes is variable. Clinicians and researchers should implement comprehensive and theoretically motivated WM assessments to better understand the WM components impacted by 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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".