Incorporating a Computerized Cognitive Battery Into the Emergency Department Care of Pediatric Mild Traumatic Brain Injuries—Is It Feasible?
Bibliographic record
Abstract
OBJECTIVES: The use of computers to test cognitive function acutely after a concussion is becoming increasingly popular, especially after sport-related concussion. Although commonly performed in the community, it is not yet performed routinely in the emergency department (ED), where most injured children present. The challenges of performing computerized cognitive testing (CCT) in a busy ED are considerable. The aim of this study was to evaluate the feasibility of CCT in the pediatric ED after concussion. METHODS: Children, aged 8 to 18 years with mild traumatic brain injury, presenting to the ED were eligible for this prospective study. Exclusion criteria included the use of drugs, alcohol, and/or physical injury, which could affect CCT performance. A 30- or 15-minute CCT battery was performed. Feasibility measures included environmental factors (space, noise, waiting time), testing factors (time, equipment reliability, personnel), and patient factors (age, injury characteristics). RESULTS: Forty-nine children (28 boys; mean age, 12.6; SD, ± 2.5) participated in the study. All children completed CCT. Mean testing times for the 30- and 15-minute battery were 29.7 and 15.2 minutes, respectively. Noise-cancelling headphones were well tolerated. A shorter CCT was more acceptable to families and was associated with fewer noise disturbances. There was sufficient time to perform testing after triage and before physician assessment in over 90% of children. CONCLUSIONS: Computerized cognitive testing is feasible in the ED. We highlight the unique challenges that should be considered before its implementation, including environmental and testing considerations, as well as personnel training.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".