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A Review of MRI and Exercise Treatment for Improved Concussion Diagnosis and Recovery

2020· review· en· W3099506423 on OpenAlexaff
Ethan Danielli, Carol DeMatteo, Geoffrey B. Hall, Michael D. Noseworthy

Bibliographic record

VenueCritical Reviews in Biomedical Engineering · 2020
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsConcussionMedicinePhysical therapyPhysical medicine and rehabilitationTraumatic brain injuryInjury preventionPoison controlMedical emergencyPsychiatry

Abstract

fetched live from OpenAlex

Concussions are a major health concern due to the unpredictable onset and resolution of debilitating post-concussion symptoms. This review discusses physiological, structural and functional brain changes post-concussion, novel non-invasive medical imaging techniques to improve diagnosis, and the role exercise could play in concussion recovery. After sustaining a concussion, about 50% of youth and 20% of adults have symptoms that last for more than a month. Understanding concussion severity has become consequential in recent years as professional sports leagues have acknowledged their harmful short- and long-term effects. Despite these effects, concussed children and adults continue to return to activity and sport prior to a full recovery. This premature return can be enabled because routine clinical medical imaging techniques are unable to detect post-concussion brain damage. However, there have been advances in MRI approaches that clearly indicate brain damage due to concussion. In terms of recovery, rest has been the long-standing prescribed concussion treatment; however, subsymptom exacerbating exercise has been shown to be a safe and effective treatment option. Novel controlled aerobic exercise interventions have improved concussion outcomes by reducing recovery time and symptom severity.

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)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.581
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.001
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 head0.116
GPT teacher head0.431
Teacher spread0.315 · 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.

Study designSystematic review
Domainnot available
GenreReview

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

Citations3
Published2020
Admission routes1
Has abstractyes

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