Relationship between Anatomical Risk Factors, Articular Cartilage Lesions, and Patient Outcomes Following Medial Patellofemoral Ligament Reconstruction
Bibliographic record
Abstract
Objective The purpose of this study was (1) to determine which risk factors for patellar instability were associated with the presence of patellofemoral cartilage lesions and (2) to determine how cartilage lesion presence, size, and grade affect postoperative disease-specific quality of life. Design Preoperative, intraoperative, and postoperative demographic, anthropometric (body mass index, Beighton score, hip rotation), radiographic (crossover sign, trochlear bump), cartilage lesion morphology (presence, size, location, grade), and outcomes data (Banff Patella Instability Instrument 2.0 [BPII 2.0]) were prospectively collected from patients undergoing isolated medial patellofemoral ligament reconstruction. For all knees ( n = 264), single and multivariable logistic regression was used to determine if any patellar instability risk factors affected the odds of having a cartilage lesion. In patients with unilateral symptoms ( n = 121), single variable linear regression was used to determine if the presence, size, or ICRS (International Cartilage Regeneration & Joint Preservation Society) grade of cartilage lesions could predict the 12 or 24+ month postoperative BPII 2.0 score. Results A total of 84.5% of knees had patellofemoral cartilage lesions (88.3% involved the distal-medial patella). Trochlear dysplasia (high grade: odds ratio = 15.7, P < 0.001; low grade: odds ratio = 2.9, P = 0.015) was associated with the presence of a cartilage lesion. The presence, size, and grade of cartilage lesions were not associated with 12 or 24+ month postoperative BPII 2.0 scores. Conclusions Trochlear dysplasia was a risk factor for the development of patellofemoral cartilage lesions in this patient population. Cartilage lesions most commonly involve the distal-medial patella. There was no significant relationship between patellofemoral cartilage lesion presence, size, or grade and postoperative BPII 2.0 scores in short-term follow-up.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".