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Performance monitoring in children following traumatic brain injury

2008· article· en· W2015410019 on OpenAlexaff
Tisha J. Ornstein, Harvey S. Levin, Shirley Chen, Gerri Hanten, Linda Ewing‐Cobbs, Maureen Dennis, Marcia A. Barnes, Jeffrey E. Max, Gordon D. Logan, Russell Schachar

Bibliographic record

VenueJournal of Child Psychology and Psychiatry · 2008
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of GuelphHospital for Sick ChildrenToronto Metropolitan University
FundersNational Institute of Neurological Disorders and Stroke
KeywordsTraumatic brain injuryPsychologyContinuous performance taskPoison controlTask (project management)Injury preventionAudiologyStop signalConcussionDevelopmental psychologyCognitionMedicinePsychiatryMedical emergency

Abstract

fetched live from OpenAlex

BACKGROUND: Executive control deficits are common sequelae of childhood traumatic brain injury (TBI). The goal of the current study was to assess a specific executive control function, performance monitoring, in children following TBI. METHODS: Thirty-one children with mild-moderate TBI, 18 with severe TBI, and 37 control children without TBI, of comparable age and sex, performed the stop signal task, a speeded choice reaction time task. On occasion, they were presented with a signal to stop their responses. Performance monitoring was defined as the extent of slowing in go-task reaction time following failure to stop responses. RESULTS: The TBI group as a whole demonstrated less post-error slowing than did controls. This finding suggested impaired error monitoring performance. In addition, time since injury and socioeconomic status predicted less slowing after stopped responses. CONCLUSIONS: We suggest that alterations in performance monitoring expressed as the inability to notice, regulate and adjust behavior to changing situations are an effect of TBI in children.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.368
Teacher spread0.326 · 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

Citations36
Published2008
Admission routes1
Has abstractyes

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