The REVision Using Imaging to Guide Staging and Evaluation (REVISE) in ACL Reconstruction Classification
Bibliographic record
Abstract
Revision anterior cruciate ligament (ACL) procedures are increasing in incidence and possess markedly inferior clinical outcomes (76% satisfaction) and return-to-sports (57%) rates than their primary counterparts. Given their complexity, a universal language is required to identify and communicate the technical challenges faced with revision procedures and guide treatment strategies. The proposed REV: ision using I: maging to guide S: taging and E: valuation (REVISE) ACL (anterior cruciate ligament) Classification can serve as a foundation for this universal language that is feasible and practical with acceptable inter-rater agreement. A focus group of sports medicine fellowship-trained orthopaedic surgeons was assembled to develop a classification to assess femoral/tibial tunnel "usability" (placement, widening, overlap) and guide the revision reconstruction strategy (one-stage vs. two-stage) post-failed ACL reconstruction. Twelve board-certified sports medicine orthopaedic surgeons independently applied the classification to the de-identified computed tomographic (CT) scan data of 10 patients, randomly selected, who failed ACL reconstruction. An interclass correlation coefficient (ICC) was calculated (with 95% confidence intervals) to assess agreement among reviewers concerning the three major classifications of the proposed system. Across surgeons, and on an individual patient basis, there was high internal validity and observed agreement on treatment strategy (one-stage vs. two-stage revision). Reliability testing of the classification using CT scan data demonstrated an ICC (95% confidence interval) of 0.92 (0.80-0.98) suggesting "substantial" agreement between the surgeons across all patients for all elements of the classification. The proposed REVISE ACL Classification, which employs CT scan analysis to both identify technical issues and guide revision ACL treatment strategy (one- or two-stage), constitutes a feasible and practical system with high internal validity, high observed agreement, and substantial inter-rater agreement. Adoption of this classification, both clinically and in research, will help provide a universal language for orthopaedic surgeons to discuss these complex clinical presentations and help standardize an approach to diagnosis and treatment to improve patient outcomes. The Level of Evidence for this study is 3.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".