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Record W2746879227 · doi:10.1136/bjsports-2017-097884

International Olympic Committee consensus statement on pain management in elite athletes

2017· article· en· W2746879227 on OpenAlexaff
Brian Hainline, Wayne Derman, Alan Vernec, Richard Budgett, Masataka Deie, Jiří Dvořák, Chris Harle, Stanley A. Herring, Mike McNamee, Willem Meeuwisse, G. Lorimer Moseley, B Omololu, John Orchard, Andrew Pipe, Babette M Pluim, Johan Ræder, Christian H. Siebert, Mike Stewart, Mark Stuart, Judith A. Turner, Mark A. Ware, David Zideman, Lars Engebretsen

Bibliographic record

VenueBritish Journal of Sports Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsMcGill University Health CentreUniversity of CalgaryUniversity of OttawaWorld Anti-Doping Agency
Fundersnot available
KeywordsAthletesElitePhysical therapyPsychosocialHarmElite athletesMedicineConsensus conferencePain managementPhysical medicine and rehabilitationPsychologyPsychiatryPolitical science

Abstract

fetched live from OpenAlex

Pain is a common problem among elite athletes and is frequently associated with sport injury. Both pain and injury interfere with the performance of elite athletes. There are currently no evidence-based or consensus-based guidelines for the management of pain in elite athletes. Typically, pain management consists of the provision of analgesics, rest and physical therapy. More appropriately, a treatment strategy should address all contributors to pain including underlying pathophysiology, biomechanical abnormalities and psychosocial issues, and should employ therapies providing optimal benefit and minimal harm. To advance the development of a more standardised, evidence-informed approach to pain management in elite athletes, an IOC Consensus Group critically evaluated the current state of the science and practice of pain management in sport and prepared recommendations for a more unified approach to this important topic.

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)0.000
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 score0.919
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.020
GPT teacher head0.299
Teacher spread0.279 · 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

Citations190
Published2017
Admission routes1
Has abstractyes

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