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Record W4281382650 · doi:10.1177/23259671221097438

Influence of Ankle Injury on Subsequent Ankle, Knee, and Shoulder Injuries in Competitive Badminton Players Younger Than 13 Years

2022· article· en· W4281382650 on OpenAlexaff
Xiaoxuan Liu, Kazuhiro Imai, Xiao Zhou, Eiji Watanabe

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineAnkleAnkle injuryPhysical therapyOdds ratioIncidence (geometry)EpidemiologyInjury preventionLogistic regressionSports medicinePoison controlSurgeryEmergency medicineInternal medicine

Abstract

fetched live from OpenAlex

Background: In recent years, there has been a trend in badminton toward more specialized training at an earlier age. Accompanying this trend is the increased frequency of injuries in young players. Ankle injury is the most common injury in pediatric sports; however, its influence on subsequent injuries is rarely considered. Purposes: To evaluate the incidence of ankle, knee, and shoulder injuries in youth badminton and to investigate the influence of ankle injuries on subsequent ankle, knee, and shoulder injuries. Study Design: Descriptive epidemiology study; Level of evidence, 3. Methods: A custom-designed questionnaire was used to survey Japanese players 7 to 12 years of age who attended national elementary school–level badminton tournaments between May and September 2019. Information including the players’ characteristics, training history, injuries in the previous 12 months, and ankle injury histories were collected. Logistic regression was used for analysis. Results: A total of 478 players were included in the study, with 71 ankle injuries, 74 knee injuries, and 48 shoulder injuries reported. The injury incidence rates (per 1000 hours of play) were 0.23 (95% CI, 0.18-0.29) for the ankle, 0.24 (95% CI, 0.19-0.30) for the knee, and 0.16 (95% CI, 0.11-0.20) for the shoulder; 90.1% of ankle injuries, 25.7% of knee injuries, and 33.3% of shoulder injuries were acute. Previous ankle injury was significantly associated with subsequent ankle injury (adjusted Odds Ratio (OR), 3.05; 95% CI, 1.54-6.07; P < .05), knee injury (adjusted OR, 2.03; 95% CI, 1.12-3.69; P < .05), and shoulder injury (adjusted OR, 2.46; 95% CI, 1.26-4.83; P < .05). Conclusion: The study results indicated that previous injury to the ankle significantly increased the occurrence of subsequent ankle, knee, and shoulder injuries. Emphasizing protection and prevention of ankle injuries may help lower future injury risk in young badminton players.

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)0.003
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
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 head0.010
GPT teacher head0.273
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

Citations17
Published2022
Admission routes1
Has abstractyes

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