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Record W4397032817 · doi:10.1080/15438627.2024.2351964

Aetiology, epidemiology and treatment of musculoskeletal injuries in badminton players: a systematic review and meta-analysis

2024· review· en· W4397032817 on OpenAlexaboutno aff
Ana Marchena‐Rodríguez, David Cabello‐Manrique, Ana Belén Ortega‐Ávila, Magdalena Martínez-Rico, Pablo Cervera‐Garvi, Gabriel Gijón-Noguerón

Bibliographic record

VenueResearch in Sports Medicine · 2024
Typereview
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsnot available
Fundersnot available
KeywordsEpidemiologyMeta-analysisEtiologyMedicinePhysical therapyHuman factors and ergonomicsInjury preventionPoison controlPhysical medicine and rehabilitationMedical emergencyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

The present study has two main goals: to conduct a systematic review of musculoskeletal injuries experienced by badminton players, and to examine the management of such injuries. Searches were conducted of the PROSPERO, PubMed, Scopus, and SPORTDiscus databases, from their inception until March 2023. The papers analysed were all based on a study population consisting of individuals aged 18 years or more, diagnosed with badminton-related injuries. The methodological quality assessments was using the Newcastle-Ottawa Scale and REVMAN. A total of 28 studies were included in the systematic review. In total, the analysis included 2435 participants. Of these athletes, 35.6% (1012) were female and 64.4% (1503) were male. By type of injury, sprains were the most commonly studied and the most prevalent, accounting for 36.06% of the sample. These were followed by muscle injuries, representing 23.86% of the total. Injuries to the joints were the least prevalent, accounting for 4.97% of the sample. Lower limb injuries accounted for 52.15% of the total. Of these, ankle injuries were the most common. Despite the generally low quality of the studies considered, the evidence suggests that musculoskeletal injuries, especially to the lower limb, most commonly affect badminton players of all levels.

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)0.015
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.716
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0180.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.299
GPT teacher head0.551
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 designSystematic review
Domainnot available
GenreReview

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

Citations10
Published2024
Admission routes1
Has abstractyes

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