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Record W2011747853 · doi:10.1016/j.acn.2006.06.013

The effects of mild and severe traumatic brain injury on speed of information processing as measured by the computerized tests of information processing (CTIP)☆

2006· article· en· W2011747853 on OpenAlexaffabout
Tom N. Tombaugh, L. H. Rees, P STORMER, Andrew Harrison, Andrew M. Smith

Bibliographic record

VenueArchives of Clinical Neuropsychology · 2006
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of OttawaQueen's UniversityCarleton University
Fundersnot available
KeywordsTraumatic brain injuryInformation processingMeasure (data warehouse)MedicineTest (biology)Computer sciencePsychologyNeuroscienceData miningPsychiatry

Abstract

fetched live from OpenAlex

In spite of the fact that reaction time (RT) measures are sensitive to the effects of traumatic brain injury (TBI), few RT procedures have been developed for use in standard clinical evaluations. The computerized test of information processing (CTIP) [Tombaugh, T. N., & Rees, L. (2000). Manual for the computerized tests of information processing (CTIP). Ottawa, Ont.: Carleton University] was designed to measure the degree to which TBI decreases the speed at which information is processed. The CTIP consists of three computerized programs that progressively increase the amount of information that is processed. Results of the current study demonstrated that RT increased as the difficulty of the CTIP tests increased (known as the complexity effect), and as severity of injury increased (from mild to severe TBI). The current study also demonstrated the importance of selecting a non-biased measure of variability. Overall, findings suggest that the CTIP is an easy to administer and sensitive measure of information processing speed.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.967
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.049
GPT teacher head0.397
Teacher spread0.348 · 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.

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

Citations51
Published2006
Admission routes2
Has abstractyes

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