Development of a Clinician-Rated Drop Vertical Jump Scale for Patients Undergoing Rehabilitation After Anterior Cruciate Ligament Reconstruction: A Delphi Approach
Bibliographic record
Abstract
Study Design Delphi panel study. Background Biomechanical parameters measured during a drop vertical jump task are risk factors for anterior cruciate ligament (ACL) injury and are targeted during rehabilitation after ACL reconstruction. A clinically feasible tool that quantifies observed performance on the drop vertical jump would help inform treatment efforts. The content and scoring of such a tool should be deliberated on by a group of experts throughout its development. Objectives To establish consensus on the content and scoring of a clinician-rated drop vertical jump scale (DVJS) for use during rehabilitation after ACL reconstruction. Methods Using a modified Delphi process, a panel of experts (researchers and clinicians) on the risk factors, prevention, treatment, and biomechanics of ACL injury anonymously critiqued versions of a DVJS. The DVJS was developed iteratively, based on the feedback from the panel, using Likert scale responses to questions and providing written comments. Three to 5 rounds were planned a priori, with a requirement of 75% agreement on included items after the final round. Results Twenty of the 31 invited experts (65%) participated. Approximately 93% agreement was achieved after the fourth round. Final items on the scale included the rating of knee valgus collapse (no collapse to extreme collapse) and the presence of other undesirable movements, including lateral trunk lean, insufficient knee flexion, and limb-to-limb asymmetry. Conclusion The Delphi process resulted in a beta version of a DVJS. Expert consensus was achieved on its content and scoring to support further clinical testing of the scale. J Orthop Sports Phys Ther 2017;47(8):557-564. Epub 6 Jul 2017. doi:10.2519/jospt.2017.7183.
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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 | 0.098 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".