MétaCan
Menu
Back to cohort
Record W2615171689 · doi:10.1123/jsr.2017-0038

Physiological and Performance Measures for Baseline Concussion Assessment

2017· article· en· W2615171689 on OpenAlexaboutno aff
Danielle M. Dobney, Scott Thomas, Tim Taha, Michelle Keightley

Bibliographic record

VenueJournal of Sport Rehabilitation · 2017
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsnot available
Fundersnot available
KeywordsConcussionContext (archaeology)Physical therapyMoodMedicineBaseline (sea)AthletesBlood pressurePhysical medicine and rehabilitationObservational studyPoison controlInjury preventionInternal medicineClinical psychologyEmergency medicine

Abstract

fetched live from OpenAlex

CONTEXT: Baseline testing is a common strategy for concussion assessment and management. Research continues to evaluate novel measures for potential to improve baseline testing methods. OBJECTIVES: The primary objective was to (1) determine the feasibility of including physiological, neuromuscular, and mood measures as part of baseline concussion testing protocol, (2) describe typical values in a varsity athlete sample, and (3) estimate the influence of concussion history on these baseline measures. DESIGN: Prospective observational study. SETTING: Ryerson University Athletic Therapy Clinic. PARTICIPANTS: One hundred varsity athletes. MAIN OUTCOME MEASURES: Frequency and domain measures of heart rate variability, blood pressure, grip strength, profile of mood states-short form, and the Sport Concussion Assessment Tool-2. RESULTS: Physiological, neuromuscular performance, and mood measures were feasible at baseline. Participants with a history of 2 or more previous concussions displayed significantly higher diastolic blood pressure. Females reported higher total mood disturbance compared with males. CONCLUSIONS: Physiological and neuromuscular performance measures are safe and feasible as baseline concussion assessment outcomes. History of concussion may have an influence on diastolic blood pressure.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score0.169

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.079
GPT teacher head0.411
Teacher spread0.332 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Sport RehabilitationSame topicTraumatic Brain Injury ResearchFrench-language works237,207