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Record W3094581193 · doi:10.3389/fneur.2020.541533

Examining Test-Retest Reliability and Reliable Change for Cognition Endpoints for the CENTER-TBI Neuropsychological Test Battery

2020· article· en· W3094581193 on OpenAlexaff
Jonas Stenberg, Justin E. Karr, Rune Hatlestad Karlsen, Toril Skandsen, Noah D. Silverberg, Grant L. Iverson

Bibliographic record

VenueFrontiers in Neurology · 2020
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsGF Strong Rehabilitation CentreUniversity of British Columbia
FundersHelse Midt-NorgeSpaulding Research InstituteNorges Teknisk-Naturvitenskapelige UniversitetU.S. Department of Defense
KeywordsCambridge Neuropsychological Test Automated BatteryTraumatic brain injuryNeuropsychologyPercentileNeuropsychological testCognitionNeuropsychological assessmentPsychologyMedicinePhysical therapyPhysical medicine and rehabilitationPsychiatryWorking memorySpatial memoryStatistics

Abstract

fetched live from OpenAlex

Objective: Seven candidate cognition composite scores have been developed and evaluated as part of a research program designed to validate a cognition endpoint for traumatic brain injury (TBI) research and clinical trials, but these composites have yet to be examined longitudinally. This study examined test-retest reliability and methods for determining reliable change for these seven candidate composite scores, using the neuropsychological test battery from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI). Methods: Participants (18-59 years-old) with mild TBI (n=124), orthopedic trauma without head injury (n=67), and healthy community controls (n=63) from the Trondheim MTBI follow-up study completed the CENTER-TBI neuropsychological test battery at two weeks and three months after injury. The battery included both traditional paper-and-pencil tests and computerized tests from the Cambridge Neuropsychological Test Automated Battery (CANTAB). Seven composite scores were calculated for the paper-and-pencil tests, the CANTAB tests, and all tests combined (i.e., 21 composites in total on each assessment): the overall test battery mean (OTBM); global deficit score (GDS); neuropsychological deficit score-weighted (NDS-W); low score composite (LSC); and the number of scores ≤5th percentile, ≤16th percentile, or <50th percentile. The OTBM was calculated by averaging T scores for all tests. The other composite scores were deficit-based scores, assigning different weights to low scores. Results: All composites revealed better cognitive performance at the three-month assessment compared to the two-week assessment and the magnitude of improvement was similar across groups. Differences, in terms of effect sizes, were largest on the OTBMs. In the combined composites, the test-retest correlation was highest for the OTBM (Spearman’s rho=0.87, in the community control group) and lowest for the number of scores ≤5th percentile (rho=0.41). Conclusion: The high test-retest reliability of the OTBM appears to favor its use in TBI research; however, future studies are needed to examine these candidate composite scores in participants with more severe TBIs and cognitive deficits and the association of the composites with functional outcomes.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.025
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.149
GPT teacher head0.330
Teacher spread0.181 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2020
Admission routes1
Has abstractyes

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