Investigating the diagnostic accuracy of a paper-and-pencil and a computerized cognitive test battery for pediatric mild traumatic brain injury.
Bibliographic record
Abstract
OBJECTIVE: This study assessed classification accuracy of paper-and-pencil and computerized cognitive batteries at subacute (SA; 1-11 days) and early chronic (EC; ∼4 months) phases of pediatric mild traumatic brain injury (pmTBI). Two statistical approaches focused on single-subject performance (individual task scores, total impairments) were used to maximize clinical utility. METHOD: Two hundred thirty-five pmTBI and 169 healthy controls (HC) participants aged 8-18 were enrolled, with a subset (190 pmTBI; 160 HC) returning for the EC visit. The paper-and-pencil battery included several neuropsychological tests selected from recommended common data elements, whereas computerized testing was performed with the Cogstate Brief Battery. Hierarchical logistic regressions (base model: Parental education and premorbid reading abilities; full model: Base model and cognitive testing variables) were used to examine sensitivity/specificity, with diagnosis as the dependent variable. RESULTS: Number Sequencing and Cogstate One-Card Learning accuracy significantly predicted SA diagnosis (full model accuracy = 71.6%-71.7%, sensitivity = 80.6%-80.8%, specificity = 59.1%-59.6%), while only immediate recall was significant at EC visit (accuracy = 68.5%, sensitivity = 74.6%, specificity = 61.5%). Other measures (Letter Fluency, Cogstate Detection, and One-Card Learning accuracy) demonstrated higher proportions of impairment for pmTBI subacutely (pmTBI: 11.5%-19.8%; HC: 3.7%-6.1%) but did not improve classification accuracy. Evidence of multiple impairments across the entire testing battery significantly predicted diagnosis at both visits (full model accuracy = 66.2%-68.6%, sensitivity = 71.2%-78.9%, specificity = 54.3%-61.5%). CONCLUSIONS: Current results suggest similar modest diagnostic accuracy for computerized and paper-and-pencil batteries across multiple pmTBI phases. Moreover, findings suggest the total number of impairments may be more clinically useful than any single test or cognitive domain in terms of diagnostic accuracy at both assessment points. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".