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Record W2297755980 · doi:10.1017/cjn.2015.63

Increased focal and diffuse cerebral demand after concussion

2015· article· en· W2297755980 on OpenAlexaffvenue
Alireza Sojoudi, Frank P. MacMaster, Karen Barlow, Aneesh Khetani

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2015
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsCalgary Laboratory Services
Fundersnot available
KeywordsAsymptomaticConcussionMedicineWorking memoryPost-concussion syndromeCognitionCerebellumAudiologyPhysical medicine and rehabilitationPediatricsInternal medicinePhysical therapyPoison controlInjury preventionPsychiatry

Abstract

fetched live from OpenAlex

Aim: To examine cortical activation during a memory task in children with and without post-concussion symptoms (PCS) following concussion. Methods: A case-controlled study within the PlayGame Trial ( www.playgametrial.ca ). Children (aged 8-18 years) with PCS at 1-month post-injury were eligible. The fMRI task was a working memory task. Pre-processing and single-subject analysis were performed in FSL. Group activation and inter-group difference maps were extracted. Results: 11 symptomatic, 12 asymptomatic, and 11 controls without concussion participated. Groups were similar in age (14.9, 14.0, and 13.8yrs; p=0.46), sex (p=0.984) and time post-injury (symptomatic: 37d; asymptomatic 35d; p=0.573). Compared with controls, symptomatic children demonstrated greater activation especially in the bilateral orbito-frontal cortex and cerebellum. A similar, less pronounced pattern was observed in asymptomatic subjects. Conclusions: Similar to adult studies, increased network activation may represent decreased “efficiency” and explain the cognitive fatigue in PCS. Further, children who are “asymptomatic” may not yet be fully recovered.

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)0.001
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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.074
GPT teacher head0.315
Teacher spread0.242 · 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

Citations0
Published2015
Admission routes2
Has abstractyes

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