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Record W4225598399 · doi:10.1037/neu0000803

Investigating the diagnostic accuracy of a paper-and-pencil and a computerized cognitive test battery for pediatric mild traumatic brain injury.

2022· article· en· W4225598399 on OpenAlexaff
Veronik Sicard, David D. Stephenson, Danielle C. Hergert, Andrew B. Dodd, Cidney R. Robertson‐Benta, Sharvani Pabbathi Reddy, Keith Owen Yeates, Jason A. Cromer, Timothy B. Meier, Richard A. Campbell, J. P. Phillips, Robert E. Sapién, Andrew R. Mayer

Bibliographic record

VenueNeuropsychology · 2022
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of Calgary
FundersNational Institute of Neurological Disorders and StrokeNational Institute of General Medical SciencesNational Institutes of Health
KeywordsAudiologyNeuropsychologyFluencyCognitive testLogistic regressionCognitionDiagnostic accuracyMedicinePsychologyRecallInternal medicinePsychiatryCognitive psychology

Abstract

fetched live from OpenAlex

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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.117
GPT teacher head0.383
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2022
Admission routes1
Has abstractyes

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