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Record W7117491459 · doi:10.1097/pep.0000000000001249

Exploring the Feasibility of a Vestibular/Oculomotor Caregiver-Supervised Exercise Program in Children Post Moderate-Severe Traumatic Brain Injury

2025· article· en· W7117491459 on OpenAlexaff
Gilad Sorek, Isabelle Gagnon, Kathryn Schneider, Mathilde Chevignard, Nurit Stern, Yahaloma Fadida, Liran Kalderon, Sharon Shaklai, Katz-Leurer Michal

Bibliographic record

VenuePediatric Physical Therapy · 2025
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsTraumatic brain injuryIntervention (counseling)Injury preventionPoison controlMEDLINEHuman factors and ergonomics

Abstract

fetched live from OpenAlex

PURPOSE: To explore the feasibility of a vestibular/oculomotor caregiver-supervised exercise program in children post moderate-severe traumatic brain injury (TBI). METHODS: The study included 37 children aged 6-18 years, with a median of 42-days post moderate-severe TBI. The intervention-group participated in a vestibular/oculomotor caregiver-supervised exercise program for 8 weeks; the control-group continued with standard-care only. Feasibility was evaluated based on the number of adverse-events and practices reported, and the ability to perform assessments. Vestibular/oculomotor function was evaluated by the number abnormal tests in the Vestibular/Ocular-Motor-Screening. Balance was evaluated by the Pediatric Balance Scale and Functional-Gait-Assessment. RESULTS: All participants completed the tests and no adverse-events were observed during the study. However, only 6 participants in the intervention-group performed ≥80% of the recommended practice. All assessments were significantly improved (P < .05) in both groups, with no significant differences between them. CONCLUSIONS: Although the vestibular/oculomotor caregiver-supervised intervention program was safe, the cooperation-rate was low, indicating difficulties with feasibility.

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 score0.721
Threshold uncertainty score0.883

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
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 head0.115
GPT teacher head0.372
Teacher spread0.257 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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