Physician And Non-physician Inter- And Intra-observer Reliability Of A Field-based Drop Vertical Jump Screening Test For ACL Injury Risk
Notice bibliographique
Résumé
Objectives: There is an epidemic of ACL injuries in pediatric and adolescent athletes. Poor neuromuscular control is an easily modifiable risk factor for ACL injury, and can be screened for by observing dynamic knee valgus on landing in a drop-vertical jump test. This study aims to validate a simple, clinically useful population-based screening test to identify at-risk athletes prior to participation in organized sports. We evaluated the inter- and intra-rater reliability of risk assessment by various observer groups, including physicians and non-physicians, commonly involved in the care of youth athletes. Methods: The screening involves observers watching a simple drop vertical jump in sports field conditions, without the use of additional analytic equipment. 15 athletes age 9-17 were filmed performing a drop vertical jump test. These videos were viewed by 242 observers including orthopaedic surgeons, residents/fellows, coaches, athletic trainers (ATC), and physical therapists (PT), with the observer asked to subjectively estimate the risk level of each jumper. Analytical objective injury risk was calculated using normalized knee separation distance (measured using Dartfish, Alpharetta, GA), based on previously published studies. Risk assessments by observers were compared to each other to determine inter-rater reliability and to the objectively calculated risk level to determine sensitivity and specificity. 71 observers repeated the test at a minimum of 6 weeks later to determine intra-rater reliability. Results: Overall, between groups (ATCs, attending physicians, coaches, residents/fellows, and PTs), the inter-rater reliability was high, κ = 0.92 (95% CI 0.829-0.969, p<0.05), indicating that no one group gave better (or worse) answers, including comparisons between physicians and non-physicians. With a screening cutoff of only jumpers identified by observers as “high risk”, the sensitivity was 63.06% and specificity 82.81%. Reducing the screening cutoff to also include jumpers identified as “medium risk” increased sensitivity to 95.04% and decreased the specificity to 46.07%. Intra-rater reliability was substantial, κ = 0.55 (95% CI 0.49-0.61, p<0.05), indicating that individual observers made reproducible risk assessments. Conclusion: This study supports the use of a simple, field-based observational drop vertical jump screening test to identify athletes at higher risk for ACL injury. Among those who could potentially be involved in this screening process, our study shows good inter- and intra-rater reliability and high sensitivity, and can be performed without significant training by coaches and athletic trainers in addition to healthcare professionals. Identification of these high-risk athletes may play a role in enrollment in appropriate preventative neuromuscular training programs, which have been shown to decrease the incidence of ACL injuries in this population.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,044 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».