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Record W3080232608 · doi:10.3390/brainsci10090593

Concussion Office Based Rehabilitation Assessment: A Novel Clinical Tool for Concussion Assessment and Management

2020· article· en· W3080232608 on OpenAlexaff
Matthew C Katz, Stéphane Lenoski, Haitham Ali, Neil Craton

Bibliographic record

VenueBrain Sciences · 2020
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsConcussionCobraMedicineRehabilitationPhysical medicine and rehabilitationPhysical therapyPoison controlInjury preventionMedical emergencyComputer science

Abstract

fetched live from OpenAlex

The Concussion Office Based Rehabilitation Assessment (COBRA) is a novel tool constructed to ensure a comprehensive assessment of patients who may have sustained a concussion. The SCAT-5 (Sport Concussion Assessment Tool) has long been the gold standard for concussion assessment, however, it was designed as a sideline tool and its utility can be seen to decrease after a few days post-concussion. It also does not prompt evaluation of all the phenotypes of concussion. As such, the COBRA was created to assess the majority of potential manifestations of concussion in the office setting a day or two after an injury has been sustained. The COBRA utilizes the eight phenotypes of concussion as a guide to assess each of the potential biopsychosocial features that can be associated with these injuries and can be used to guide evidence-based treatments. Through early identification of concussion phenotypes, the clinician may start optimal treatment and hopefully prevent prolonged recovery and persisting symptoms.

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)0.005
metaresearch head score (Gemma)0.002
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 score0.476
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.179
GPT teacher head0.493
Teacher spread0.314 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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