Arthroscopic Management for Bipartite Patella: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Bipartite patella is a rare congenital condition that becomes painful following direct trauma or an overuse injury. If it remains painful despite nonoperative treatment, surgery may be warranted. The current gold standard is open fragment excision or lateral release; however, arthroscopic management is also possible. PURPOSE: To investigate the safety and efficacy of arthroscopic treatment of painful bipartite patella. STUDY DESIGN: Systematic review; Level of evidence, 4. METHODS: Using Medline and Embase, we systematically reviewed the literature as of March 8, 2020, using the subject headings "bipartite patella" and "arthroscopy" and related key terms. All levels of evidence involving human studies in English were included. Articles were excluded if only the abstract was published or the study was related to nonsurgical treatment or nonrelated diagnoses. Data related to journal/article information, demographic/clinical data, arthroscopic technique, length of follow-up, treatment outcomes, and complications were extracted. RESULTS: Eleven articles with 43 patients were included in the review. Most patients (n = 34; 79%) underwent arthroscopic lateral release, while 16% (n = 7) had arthroscopic excision of the accessory fragment and 5% (n = 2) had arthroscopic excision and release. All patients except for one, who experienced postoperative trauma, were pain-free after arthroscopic treatment and were able to return to sports after a mean 2.6 months. CONCLUSION: This review demonstrated that arthroscopic management of painful bipartite patella is a safe and effective alternative to open surgical excision or release. However, all articles were case studies or small case series, owing to the rarity of the condition. In the future, higher-level studies comparing arthroscopic techniques and postoperative rehabilitation programs should be performed.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".