Lateral Patellar Instability
Bibliographic record
Abstract
➤: Patellar instability represents a common problem with an evolving understanding and multifactorial pathoetiology. Treatment plans should be based on the identification of contributing anatomical factors and tailored to each individual patient. ➤: Risks for recurrent instability are dependent on several patient-specific factors including patella alta, increased tibial tubercle-to-trochlear groove (TT-TG) distance, trochlear dysplasia, younger skeletal age, and ligamentous laxity. ➤: Cartilage or osteochondral lesions and/or fractures are commonly observed in first-time patellar dislocation, and magnetic resonance imaging (MRI) should be strongly considered. Advanced imaging modalities, such as computed tomography (CT) or MRI, should also be obtained preoperatively to identify predisposing factors and guide surgical treatment. ➤: Medial patellofemoral ligament (MPFL) reconstruction with anatomical femoral tunnel positioning is associated with lower recurrence rates compared with MPFL repair and has become a common and successful reconstructive surgical option in cases of instability. ➤: Lateral retinacular tightness can be addressed with lateral retinacular release or lengthening, but these procedures should not be performed in isolation. ➤: Tibial tubercle osteotomy is a powerful reconstructive tool in the setting of underlying skeletal risk factors for instability and can be of particular benefit in the presence of increased TT-TG distance (>20 mm), and/or in the setting of patella alta. ➤: The indications for trochleoplasty are still developing along with the clinical evidence, but trochleoplasty may be indicated in some cases of severe trochlear dysplasia. Several surgical techniques have indications in specific clinical scenarios and populations, and indications, risks, and benefits to each are progressing with our understanding. ➤: Combined femoral derotational osteotomy and MPFL reconstruction can be considered for patients with a femoral anteversion angle of >30° to improve patient outcomes and reduce recurrence rates.
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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 | 0.001 | 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".