Clinical Outcomes After the Nonoperative Management of Lateral Patellar Dislocations: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: The first-line treatment for patellar dislocations is often nonoperative and consists of physical therapy and immobilization techniques, with various adjuncts employed. However, the outcomes of nonoperative therapy are poorly described, and there is a lack of quality evidence to define the optimal intervention. PURPOSE: To perform a comprehensive review of the literature and assess the quality of studies presenting patient outcomes from nonoperative interventions for patellar dislocations. STUDY DESIGN: Systematic review; Level of evidence, 4. METHODS: The MEDLINE, AMED, Embase, CINAHL, Cochrane Library, PEDro, and SPORTDiscus electronic databases were searched through July 2017 by 3 independent reviewers. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed. Study quality was assessed using the CONSORT (Consolidated Standards for Reporting Trials) criteria for randomized controlled trials and the Newcastle-Ottawa Scale for cohort studies and case series. RESULTS: A total of 25 studies met our inclusion criteria, including 12 randomized controlled trials, 7 cohort studies, and 6 case series, consisting of 1066 patients. Studies were grouped according to 4 broad categories of nonoperative interventions based on immobilization, weightbearing status, quadriceps exercise type, and alternative therapies. The most commonly used outcome measure was the Kujala score, and the pooled redislocation rate was 31%. CONCLUSION: This systematic review found that patient-reported outcomes consistently improved after all methods of treatment but did not return to normal. Redislocation rates were high and close to the redislocation rates reported in natural history studies. There is a lack of quality evidence to advocate the use of any particular nonoperative technique for the treatment of patellar dislocations.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".