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Record W2130843820 · doi:10.1136/bjsm.2010.071720

Sports injuries and illnesses in the 2009 FINA World Championships (Aquatics)

2010· article· en· W2130843820 on OpenAlexaff
Margo Mountjoy, Astrid Junge, Juan Manuel Alonso, Lars Engebretsen, I Drâgan, David Gerrard, Mohamed Kouidri, Eide Luebs, Farhad Moradi Shahpar, Jiří Dvořák

Bibliographic record

VenueBritish Journal of Sports Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineIncidence (geometry)AthletesPhysical therapyInjury preventionOccupational safety and healthWater poloPoison controlFamily medicineEmergency medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Analysis of injury and illness prevalence in elite sport provides the basis for the development of prevention programmes. OBJECTIVES: To analyse the frequency and characteristics of injuries and illnesses occurring during the 13th Federation Internationale de Natation (FINA) World Championships 2009. DESIGN: Prospective recording of newly incurred injuries and illnesses. METHODS: The 13th FINA World Championships hosted 2592 athletes from 172 countries in the disciplines of swimming, diving, synchronised swimming water polo and open water swimming. All team physicians or physiotherapists were asked to complete daily a standardised reporting form for all newly incurred injuries and illnesses for their teams. To cover teams without medical staff, the physicians of the Local Organizing Committee also submitted daily report forms. RESULTS: 171 injuries were reported resulting in an incidence of 66.0 per 1000 registered athletes. The most affected body parts were the shoulder (n=25; 14.6%), and head (n=21; 12.3%). Half of the injuries occurred during training. The most common cause of injury was overuse (n=61; 37.5%). 184 illnesses were reported resulting in an incidence of 71.0 per 1000 registered athletes. The respiratory tract was most commonly affected (n=91; 50.3%) and the most frequently classified cause was infection (n=81; 49.2%). The incidence of injuries and illnesses varied substantially among the five disciplines, with the highest incidence of injury in diving and the lowest in swimming. CONCLUSIONS: As the risk of injury varied with the discipline, preventive measures should be discipline specific and focused on minimising the potential for overuse. As most of the illnesses were caused by infection of the respiratory and gastrointestinal tract, preventive interventions should focus on eliminating common modes of transmission.

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)0.000
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 score0.052
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.275
Teacher spread0.264 · 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

Citations185
Published2010
Admission routes1
Has abstractyes

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