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Record W4313644658 · doi:10.1136/bjsports-2022-106405

Football-specific extension of the IOC consensus statement: methods for recording and reporting of epidemiological data on injury and illness in sport 2020

2023· article· en· W4313644658 on OpenAlexaff
Markus Waldén, Margo Mountjoy, Alan McCall, Andreas Serner, Andrew Massey, Johannes L. Tol, Roald Bahr, Michel D’Hooghe, Natália Franco Netto Bittencourt, Francesco Della Villa, Michiko Dohi, Grégory Dupont, Mark Fulcher, Dina C. Janse van Rensburg, Donna Lu, Thor Einar Andersen

Bibliographic record

VenueBritish Journal of Sports Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsMcMaster University
Fundersnot available
KeywordsFootballVotingMedicineEpidemiologySports medicineStatement (logic)Family medicineMedical educationPolitical sciencePathologyLaw

Abstract

fetched live from OpenAlex

Several sports have published consensus statements on methods and reporting of epidemiological studies concerning injuries and illnesses with football (soccer) producing one of the first guidelines. This football-specific consensus statement was published in 2006 and required an update to align with scientific developments in the field. The International Olympic Committee (IOC) recently released a sports-generic consensus statement outlining methods for recording and reporting epidemiological data on injury and illness in sport and encouraged the development of sport-specific extensions.The Fédération Internationale de Football Association Medical Scientific Advisory Board established a panel of 16 football medicine and/or science experts, two players and one coach. With a foundation in the IOC consensus statement, the panel performed literature reviews on each included subtopic and performed two rounds of voting prior to and during a 2-day consensus meeting. The panel agreed on 40 of 75 pre-meeting and 21 of 44 meeting voting statements, respectively. The methodology and definitions presented in this comprehensive football-specific extension should ensure more consistent study designs, data collection procedures and use of nomenclature in future epidemiological studies of football injuries and illnesses regardless of setting. It should facilitate comparisons across studies and pooling of data.

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)0.017
metaresearch head score (Gemma)0.005
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 score0.897
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.122
GPT teacher head0.437
Teacher spread0.315 · 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

Citations117
Published2023
Admission routes1
Has abstractyes

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