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Record W3124428807 · doi:10.4085/527-20

A Standardized Buffalo Concussion Treadmill Test After Sport-Related Concussion in Youth: Do ActiGraph Algorithms Matter?

2021· article· en· W3124428807 on OpenAlexaff
Heidi R. Morrison, Lauren N. Miutz, Carolyn A. Emery, Jonathan D. Smirl

Bibliographic record

VenueJournal of Athletic Training · 2021
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsLibin Cardiovascular Institute of AlbertaOntario Brain InstituteAlberta Children's HospitalUniversity of Calgary
FundersInternational Olympic Committee
KeywordsConcussionAlgorithmTreadmillMedicinePhysical therapyAccelerometerPhysical activityCohortPoison controlInjury preventionInternal medicineMathematicsComputer scienceEmergency medicine

Abstract

fetched live from OpenAlex

Context: Current guidelines for recovery after sport-related concussion (SRC) recommend 24 to 48 hours of rest, followed by a gradual return to activity with heart rate (HR) maintained below the symptom threshold. In addition, monitoring physical activity (PA) after SRC using ActiGraph accelerometers can provide further objective insight into the amounts of activity associated with recovery trajectories. Cutpoint algorithms for these devices allow minute-by-minute PA to be classified into intensity domains; however, researchers have shown that different algorithms used to evaluate the same healthy participant dataset can produce various classifications. Objective: To identify the more physiologically appropriate cutpoint algorithm (Evenson or Romanzini) to analyze ActiGraph data among concussed adolescents in comparison with their HR responses on the Buffalo Concussion Treadmill Test (BCTT). Design: Prospective cohort study. Setting: University sport concussion clinic. Patients or Other Participants: Eleven high school students (5 boys, 6 girls; median [range] age = 16 years [15-17 years], height = 177.8 cm [157.5-198.1 cm], mass = 67 kg [52-98 kg], body mass index = 22 [17-31]) involved in high-risk sports who sustained a physician-diagnosed SRC. Main Outcome Measures: Evenson and Romanzini algorithm PA intensity domains via ActiGraph data and HR during the BCTT. Results: = .48). The Evenson algorithm classified most of the time as moderate-intensity PA (mean = 57.03%, range = 0.00%-94.12%), whereas the Romanzini algorithm classified virtually all PA as vigorous intensity (mean = 88.25%, range = 2.94%-97.06%]). Physical activity based on HR (stages 1-7 = 20%-39% HR reserve [HRR], stages 8-13 = 40%-59% HRR, stages ≥14 = 60%-85% HRR) indicated the BCTT primarily involved light to moderate intensity and, therefore, was better represented by the Evenson algorithm. Conclusions: The Evenson algorithm better characterized the HR response during a standardized exercise test in concussed individuals and, thus, should be used to analyze ActiGraph PA data in pediatric populations with concussion.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.324
Teacher spread0.278 · 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 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

Citations3
Published2021
Admission routes1
Has abstractyes

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