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Record W2763988427 · doi:10.1177/2329048x17732713

Performance Monitoring in Children Following Traumatic Brain Injury Compared to Typically Developing Children

2017· article· en· W2763988427 on OpenAlexafffund
Amy Wilkinson, Maureen Dennis, Margot J. Taylor, Anne‐Marie Guerguerian, Kathy Boutis, Karen Choong, Craig Campbell, Douglas D. Fraser, Jamie Hutchison, Russell Schachar

Bibliographic record

VenueChild Neurology Open · 2017
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsWestern UniversityMcMaster Children's HospitalHospital for Sick ChildrenUniversity of Toronto
FundersOntario Neurotrauma FoundationMcMaster University
KeywordsTraumatic brain injuryStop signalMedicineTypically developingPsychologyPhysical medicine and rehabilitationDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

Children with traumatic brain injury are reported to have deficits in performance monitoring, but the mechanisms underlying these deficits are not well understood. Four performance monitoring hypotheses were explored by comparing how 28 children with traumatic brain injury and 28 typically developing controls (matched by age and sex) performed on the stop-signal task. Control children slowed significantly more following incorrect than correct stop-signal trials, fitting the error monitoring hypothesis. In contrast, the traumatic brain injury group showed no performance monitoring difference with trial types, but significant group differences did not emerge, suggesting that children with traumatic brain injury may not perform the same way as controls.

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)0.000
metaresearch head score (Gemma)0.006
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 score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.093
GPT teacher head0.389
Teacher spread0.296 · 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

Citations1
Published2017
Admission routes2
Has abstractyes

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