Validation of a Risk Calculator to Personalize Graft Choice and Reduce Rupture Rates for Anterior Cruciate Ligament Reconstruction
Bibliographic record
Abstract
BACKGROUND: Anterior cruciate ligament reconstructions (ACLRs) fail at an alarmingly high rate in young active individuals. The Multicenter Orthopaedic Outcomes Network (MOON) knee group has developed an autograft risk calculator that uses patient characteristics and lifestyle to predict the probability of graft rupture if the surgeon uses a hamstring tendon (HT) or a bone-patellar tendon-bone (BPTB) graft to reconstruct the ligament. If validated, this risk calculator can be used during the shared decision-making process to make optimal ACLR autograft choices and reduce rupture rates. The STABILITY 1 randomized clinical trial offers a large, rigorously collected data set of similar young active patients who received HT autograft with or without lateral extra-articular tenodesis (LET) for ACLR. PURPOSE/HYPOTHESIS: The purpose was to validate the ACLR graft rupture risk calculator in a large external data set and to investigate the utility of BPTB and LET for ACLR. We hypothesized that the risk calculator would maintain adequate discriminative ability and calibration in the external STABILITY 1 data set when compared with the initial MOON development data set. STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 1. METHODS: The model predictors for the risk calculator include age, sex, body mass index, sport played at the time of injury, Marx Activity Score, preoperative knee laxity, and graft type. The STABILITY 1 trial data set was used for external validation. Discriminative ability, calibration, and diagnostic test validity of the model were assessed. Finally, predictor strength in the initial and validation samples was compared. RESULTS: The model showed acceptable discriminative ability (area under the curve = 0.73), calibration (Brier score = 0.07), and specificity (85.3%) to detect patients who will experience a graft rupture. Age, high-grade preoperative knee laxity, and graft type were significant predictors of graft rupture in young active patients. BPTB and the addition of LET to HT were protective against graft rupture versus HT autograft alone. CONCLUSION: The MOON risk calculator is a valid predictor of ACLR graft rupture and is appropriate for clinical practice. This study provides evidence supporting the idea that isolated HT autografts should be avoided for young active patients undergoing ACLR. REGISTRATION: NCT00463099 (MOON); NCT02018354 (STABILITY 1) (ClinicalTrials.gov identifiers).
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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 | 0.061 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".