EXTERNAL VALIDATION OF THE NORWEGIAN ANTERIOR CRUCIATE LIGAMENT RECONSTRUCTION REVISION PREDICTION MODEL USING PATIENTS FROM THE STABILITY 1 TRIAL
Bibliographic record
Abstract
Machine learning has emerged as a potential tool for improving outcome prediction accuracy in anterior cruciate ligament (ACL) reconstruction (ACLR). A machine learning-based ACL revision prediction model has been developed using data from the Norwegian Knee Ligament Register (NKLR) but lacks external validation outside of Scandinavia. This study aimed to assess the external validity of the previously published ACL revision prediction model (https://swastvedt.shinyapps.io/calculator_rev/) using data from the Stability 1 randomized clinical trial. The hypothesis was that the model's performance would be similar, indicating validity of the algorithm. This was a level 3 cohort study. The NKLR Cox Lasso model was selected for external validation owing to its superior performance in the original study. Stability 1 patients with all five predictors required by the Cox Lasso model were included. The Stability 1 RCT was a prospective study which randomized patients to receive either a hamstring tendon autograft (HT) alone, or HT plus a lateral extra-articular tenodesis (LET). Since all patients in the Stability 1 trial received HT plus/minus LET, three configurations were tested: 1: all patients coded as HT, 2: HT + LET group coded as bone-patellar tendon-bone autograft (BPTB), 3: HT + LET group coded as unknown/other graft choice. Model performance was assessed via concordance and calibration. In total, 591 patients were included, and 39 patients (6.6%) underwent revision surgery. Model performance was best when patients randomized to HT + LET were coded as BPTB. Validation concordance was similar to the original NKLR prediction model for one- and two-year revision prediction (Stability: 0.71; NKLR: 0.68-0.69). Concordance confidence interval (CI) ranged from 0.63-0.79. The model was well calibrated for one-year prediction while the two-year prediction demonstrated evidence of miscalibration. When patients in Stability 1 who received HT + LET were coded as BPTB in the NKLR prediction model, the concordance was similar to the index study. However, due to a wide CI, the true performance of the prediction model with this Canadian and European cohort is unclear and a larger dataset is required to definitively determine the external validity. Further, model calibration was better for predicting revision surgery within one-year, in keeping with the original study and with prediction modeling in general, as predicting outcome over longer time-periods is typically more challenging due to the increased outcome variability observed over time.
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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 | 0.130 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".