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Record W3127329911 · doi:10.1136/bmjopen-2020-039767

Remotely delivered environmental enrichment intervention for traumatic brain injury: Study protocol for a randomised controlled trial

2021· article· en· W3127329911 on OpenAlexafffundabout
Zorry Belchev, Mary Boulos, Julia Rybkina, Eliyas Jeffay, Brenda Colella, Jason D. Ozubko, Michael J. C. Bray, Nicholas Di Genova, Adina Levi, Alana Changoor, Thomas Worthington, Asaf Gilboa, Robin Green

Bibliographic record

VenueBMJ Open · 2021
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsBaycrest HospitalYork UniversityMcMaster UniversityToronto Rehabilitation InstituteUniversity of Toronto
FundersBranch Out Neurological FoundationCanada Research ChairsOntario Neurotrauma FoundationMarch of Dimes CanadaMarch of Dimes Foundation
KeywordsMedicineRandomized controlled trialTraumatic brain injuryPhysical medicine and rehabilitationHippocampal formationIntervention (counseling)Spatial memoryCognitionRehabilitationPhysical therapyPsychiatrySurgeryWorking memoryInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Individuals with moderate-severe traumatic brain injury (m-sTBI) experience progressive brain and behavioural declines in the chronic stages of injury. Longitudinal studies found that a majority of patients with m-sTBI exhibit significant hippocampal atrophy from 5 to 12 months post-injury, associated with decreased cognitive environmental enrichment (EE). Encouragingly, engaging in EE has been shown to lead to neural improvements, suggesting it is a promising avenue for offsetting hippocampal neurodegeneration in m-sTBI. Allocentric spatial navigation (ie, flexible, bird's eye view approach), is a good candidate for EE in m-sTBI because it is associated with hippocampal activation and reduced ageing-related volume loss. Efficacy of EE requires intensive daily training, prohibitive within most current health delivery systems. The present protocol is a novel, remotely delivered and self-administered intervention designed to harness principles from EE and allocentric spatial navigation to offset hippocampal atrophy and potentially improve hippocampal functions such as navigation and memory for patients with m-sTBI. METHODS AND ANALYSIS: Eighty-four participants with chronic m-sTBI are being recruited from an urban rehabilitation hospital and randomised into a 16-week intervention (5 hours/week; total: 80 hours) of either targeted spatial navigation or an active control group. The spatial navigation group engages in structured exploration of different cities using Google Street View that includes daily navigation challenges. The active control group watches and answers subjective questions about educational videos. Following a brief orientation, participants remotely self-administer the intervention on their home computer. In addition to feasibility and compliance measures, clinical and experimental cognitive measures as well as MRI scan data are collected pre-intervention and post-intervention to determine behavioural and neural efficacy. ETHICS AND DISSEMINATION: Ethics approval has been obtained from ethics boards at the University Health Network and University of Toronto. Findings will be presented at academic conferences and submitted to peer-reviewed journals. TRIAL REGISTRATION NUMBER: Version 3, ClinicalTrials.gov Registry (NCT04331392).

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)0.017
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.080
Threshold uncertainty score0.267

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.018
Meta-epidemiology (narrow)0.0060.003
Meta-epidemiology (broad)0.0150.005
Bibliometrics0.0020.003
Science and technology studies0.0030.003
Scholarly communication0.0040.003
Open science0.0040.002
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0800.012

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 head0.217
GPT teacher head0.509
Teacher spread0.292 · 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 designRandomized trial
Domainnot available
GenreProtocol

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

Citations11
Published2021
Admission routes3
Has abstractyes

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