3D Patellar instability Anatomical Severity Score (3D-PASS): A Novel Machine Learning Score Using 3D Bone Position From MRI to Predict Outcomes of Patellar Instability Treatment (Using a Subset of Data in the JUPITER Cohort)
Bibliographic record
Abstract
Background: Patellar instability treatment outcomes vary. Early prediction of patient responses to treatment is needed to personalize interventions, reduce recurrent instability, and improve outcomes. Existing scores that predict outcomes rely on 2-dimensional (2D) imaging measures that do not fully capture 3-dimensional (3D) anatomical data. Purpose/Hypothesis: We aimed to develop a 3D medical imaging-based anatomical score of patellar instability severity. It was hypothesized that our 3D score would demonstrate stronger associations with instability history and posttreatment patient-reported outcomes (PROs) than a 2D imaging score. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: Pretreatment magnetic resonance images from 26 control patients and 244 patients with first-time and recurrent patellar instability were retrospectively analyzed from 2 sites associated with the prospective JUPITER (Justifying Patellar Instability Treatment by Results) study group. Using statistically derived 3D features that reflect relative bone positions and bone shape, as well as 2D imaging measures, we developed several preliminary anatomical severity scores. We tested these scores based on the ability to distinguish first-time from recurrent instability patients. Then, we assessed correlations between these scores and baseline and 1-year posttreatment PROs, using the Kujala Anterior Knee Pain Scale (Kujala) and Banff Patellar Instability Instrument 2.0 (BPII 2.0). A final score-the 3D Patellar instability Anatomical Severity Score (3D-PASS)-was created to best correlate with PROs. Results: = -0.25). 3D relative bone positions were more informative than 2D imaging measures and 3D bone shape, neither of which correlated with outcomes. Conclusion: A higher 3D-PASS is associated with instability history and worse outcomes across all patients (first-time and recurrent) treated nonoperatively or operatively. While 3D relative bone positions correlated with outcomes, 3D bone shapes did not.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".