Physician And Non-physician Inter- And Intra-observer Reliability Of A Field-based Drop Vertical Jump Screening Test For ACL Injury Risk
Bibliographic record
Abstract
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.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".