MétaCan
Menu
Back to cohort
Record W2763988427 · doi:10.1177/2329048x17732713

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

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

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.093
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