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Record W2141522981 · doi:10.1177/0363546503261246

Football Injuries during the World Cup 2002

2004· article· en· W2141522981 on OpenAlexaboutno aff
Astrid Junge, Jiří Dvořák, Toni Graf-Baumann

Bibliographic record

VenueThe American Journal of Sports Medicine · 2004
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsnot available
Fundersnot available
KeywordsFootballIncidence (geometry)Injury preventionMedicineQuarter (Canadian coin)Physical therapyOccupational safety and healthBody contactPoison controlSuicide preventionMedical emergencyGeography

Abstract

fetched live from OpenAlex

BACKGROUND: The Fédération Internationale de Football Association (FIFA) World Cup is one of the largest, most popular sporting events but is associated with a certain risk of injury for the players. PURPOSE: Analysis of the incidence, circumstances, and characteristics of soccer injury during the World Cup 2002. STUDY DESIGN: Prospective survey. METHODS: The physicians of all participating teams reported all injuries after each match on a standardized injury report form. The response rate was 100%. RESULTS: A total of 171 injuries were reported from the 64 matches, which is equivalent to an incidence of 2.7 injuries per match; approximately 1 to 2 injuries per match resulted in absence from training or match. More than a quarter of all injuries were incurred without contact with another player, and 73% were contact injuries. Half of the contact injuries, or 37% of all injuries, were caused by foul play as rated by the team physician and the injured player. CONCLUSION: The incidence of injuries during the World Cup 2002 was similar to those reported for the World Cup in 1994 and in 1998. Increased awareness of the importance of fair play may assist in the prevention of injury.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.008
GPT teacher head0.278
Teacher spread0.270 · 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 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

Citations206
Published2004
Admission routes1
Has abstractyes

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