MétaCan
Menu
Back to cohort
Record W2142415773 · doi:10.3109/02699052.2011.589791

Focal thinning of the posterior corpus callosum: Normal variant or post-traumatic?

2011· article· en· W2142415773 on OpenAlexaff
Miriam H. Beauchamp, Michael Ditchfield, Cathy Catroppa, Michael Kean, Celia Godfrey, Jeffrey V. Rosenfeld, Vicki Anderson

Bibliographic record

VenueBrain Injury · 2011
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineUniversité de Montréal
Fundersnot available
KeywordsCorpus callosumTraumatic brain injuryMedicineAudiologyAnatomyPsychiatry

Abstract

fetched live from OpenAlex

AIM: The corpus callosum (CC) can be affected by childhood traumatic brain injury (TBI), through focal lesions, reductions in size and arrested development. Little is known, however, about what constitutes normal CC shape and appearance and how it may be affected in the long-term after early TBI. METHODS: In this study, the appearance of the CC was investigated in individuals with TBI assessed 10 years post-injury (n = 52, mean age = 16.82 years, SD = 3.55 years, 24 male) and compared to age-matched healthy controls (n = 44, mean age = 15.77 years, SD = 1.21 years, 18 male). A simple visual analysis technique was used to code the appearance of the CC according to location of focal thinning and severity of thinning and results between groups were compared using Chi-square analysis. RESULTS: A significantly higher proportion of patients with childhood TBI had focal posterior thinning of the CC than age-matched controls (p = 0.001). CONCLUSIONS: The findings call into question previous conclusions that focal posterior thinning is a normal variant of development. Instead, attenuations of the posterior portion of the CC in the long-term may reflect altered cortical and callosal development as a result of early brain injury, although the functional significance of this remains to be determined.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.085
GPT teacher head0.341
Teacher spread0.255 · 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 designBench or experimental
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

Citations17
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueBrain InjurySame topicAdvanced Neuroimaging Techniques and ApplicationsFrench-language works237,207