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Record W2892298501 · doi:10.1136/bjsports-2018-099349

Analysis of water polo injuries during 8904 player matches at FINA World Championships and Olympic games to make the sport safer

2018· article· en· W2892298501 on OpenAlexaff
Margo Mountjoy, Jim Miller, Astrid Junge

Bibliographic record

VenueBritish Journal of Sports Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsMcMaster UniversityMcMaster University Medical Centre
Fundersnot available
KeywordsWater poloMedicineIncidence (geometry)Physical therapyConcussionTrunkAthletesInjury preventionPoison controlMedical emergency

Abstract

fetched live from OpenAlex

OBJECTIVE: To analyse injuries of water polo players reported during four Summer Olympic Games (2004, 2008, 2012, 2016) and four Federation Internationale de Natation World Championships (2009, 2013, 2015, 2017). METHODS: Injuries during training and matches were reported daily by the team physicians and the local medical staff at the sports venues using an established surveillance system. RESULTS: A total of 381 injuries were reported, equivalent to 14.1 injuries per 100 players (95% CI ±1.42). The most frequent diagnoses were laceration (12.7%) and contusion (10.9%) of head, followed by (sub-)luxation/sprain of hand (9.5%) and contusion of trunk (6.5%) or hand (6.2%). More than half of the injuries (57.0%) occurred due to contact with another player. A quarter of the injuries (25.4%) were expected to result in absence from training or match; 10 (2.9%) resulted in an estimated time-loss of 3 or more weeks. About three-quarters of injuries (75.6%) occurred during matches, 86 during training. The incidence of match injuries was on average 56.2 injuries per 1000 match hours (95% CI ±6.74). The incidence of time-loss match injuries (14.7; 95% CI ±3.44) was significantly higher in men than in women. CONCLUSIONS: A critical review of water polo in-competition rules and the implementation of a Fair Play programme may help to mitigate the high incidence of contact injuries incurred during matches. A water polo-specific concussion education programme including recognition, treatment and return to play is recommended. Finally, a prospective injury surveillance programme would help to better define water polo injuries outside of the competition period.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.264
Teacher spread0.252 · 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.

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

Citations45
Published2018
Admission routes1
Has abstractyes

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