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Record W4206739109 · doi:10.1089/neu.2021.0426

Restoration of Aberrant Shape of Caudate Subregions Associated with Cognitive Function Improvement in Mild Traumatic Brain Injury

2022· article· en· W4206739109 on OpenAlexaff
Hui Xu, Tao Yin, Pingyi Zhu, Li Y, Ming Zhang, Guanghui Bai, Bo Yin

Bibliographic record

VenueJournal of Neurotrauma · 2022
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTraumatic brain injuryAtrophyCaudate nucleusMedicineCognitionDorsumNeuroscienceVoxel-based morphometryPsychologyWhite matterInternal medicineAnatomyMagnetic resonance imagingRadiologyPsychiatry

Abstract

fetched live from OpenAlex

Mild traumatic brain injury (mTBI) is an important but less recognized public health concern. Research shows that altered subcortical structures mediate cognitive impairment in patients with mTBI. This has been performed mostly using voxel-based morphometry methods and traditional volume measurement methods, which have certain limitations. In this study, we conducted a vertex-wise shape analysis to understand the aberrant patterns of caudate subregions and recovery from mTBI. The study involved 36 patients with mTBI and 34 matched healthy controls (HCs) observed at seven days (acute phase) and followed up for one month (subacute phase) post-injury. Different aberrant shapes of the caudate subregions were observed at the acute phase, which revealed atrophy in the bilateral dorsal medial caudate, and increase in the size of the right ventral anterior caudate in patients with mTBI related to HCs. Moreover, specific and significant shape restoration of right dorsal medial caudate in mTBI was observed at the subacute phase, which significantly associated with the cognitive function improvement of the patients. These findings suggest that the restoration of the aberrant shape atrophy of the right dorsal medial caudate plays a vital role in the improvement of cognitive function of patients with mTBI, providing an alternative clinical target for these patients.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.863
Threshold uncertainty score0.511

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.079
GPT teacher head0.311
Teacher spread0.232 · 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 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

Citations17
Published2022
Admission routes1
Has abstractyes

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