Development of a Clinician-Rated Drop Vertical Jump Scale for Patients Undergoing Rehabilitation After ACL Reconstruction
Bibliographic record
Abstract
Biomechanical studies suggest performance on a drop vertical jump (DVJ) can predict anterior cruciate ligament (ACL) injury and should be targeted during rehabilitation after ACL reconstruction. A clinically feasible tool would be advantageous for quantitatively evaluating performance and change in DVJ following therapy. Such a tool should be developed by a panel of experts to establish consensus on the usefulness of the tool, and to verify that essential components are included. PURPOSE: The purpose of the present study was to establish consensus on the content and scoring of a Clinician Rated DVJ Scale for use during rehabilitation after ACL reconstruction. METHODS: Using a Delphi process, 20 experts on the risk factors, prevention, treatment and/or biomechanics of ACL injury, anonymously critiqued the proposed Clinician Rated DVJ Scale, using Likert-scales and written feedback. Three-to-five rounds were planned a priori as termination criteria, with the requirement of 75% agreement on items after the final round. RESULTS: Nine researchers and eleven clinicians including physical therapists, athletic therapists and orthopaedic surgeons participated. Response rates were 55%, 85% and 70% for rounds two, three and four, respectively. After rounds one and two, the scale was revised to include only the components that ≥ 61% of experts agreed upon. After round three, only two components had ≤ 75% agreement, and these were refined for round four. After round four, ≥ 92% agreement was achieved. Final items on the scale include a rating of knee valgus collapse (No to Extreme), and other undesirable movements including evidence of lateral trunk lean, insufficient trunk flexion, insufficient knee flexion and limb-to-limb asymmetry. A scale from 0 (No knee valgus collapse and no undesirable movements) to 9 (Extreme knee valgus collapse ± undesirable movements) is included for each leg to monitor change throughout rehabilitation. CONCLUSIONS: The Delphi process resulted in adequate agreement on the content and scoring of the Clinician Rated DVJ Scale to support its preliminary use as a measurement tool for functional testing throughout rehabilitation following ACL injury and/or reconstruction. A Beta version of the scale will be subsequently piloted.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".