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Record W2498186369 · doi:10.2519/jospt.2016.0607

Sport-Related Concussion: Optimizing Treatment Through Evidence-Informed Practice

2016· article· en· W2498186369 on OpenAlexaff
Kathryn Schneider

Bibliographic record

VenueJournal of Orthopaedic and Sports Physical Therapy · 2016
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsConcussionRecreationPeriod (music)Physical therapyMedicineRest (music)PsychologyInjury preventionPoison controlMedical emergencyPolitical scienceInternal medicine

Abstract

fetched live from OpenAlex

Concussion is one of the most common injuries in sport and recreation today. Reports of concussion have increased in recent years, likely due to increased societal awareness and the risk of longer-term sequelae. Presently, treatment includes a period of prescribed rest in the acute period following injury, followed by a protocol of graded exertion. Despite an initial period of rest and attempts at a gradual return to play, up to 30% of individuals may have ongoing symptoms past the acute period. The goal of this viewpoint is to introduce the reader to the most common symptoms of concussion and the need for a new, more active paradigm during treatment. J Orthop Sports Phys Ther 2016;46(8):613-616. doi:10.2519/jospt.2016.0607.

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)0.025
metaresearch head score (Gemma)0.075
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.075
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0070.005
Open science0.0030.005
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0070.003

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 head0.083
GPT teacher head0.396
Teacher spread0.313 · 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 designNot applicable
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

Citations25
Published2016
Admission routes1
Has abstractyes

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