MRI as the optimal imaging modality for assessment and management of osteochondral fractures and loose bodies following traumatic patellar dislocation: a systematic review
Bibliographic record
Abstract
PURPOSE: To assess the imaging modalities used for diagnosis, as well as the management decisions of patients with osteochondral fractures (OCF) and loose bodies following traumatic patellar dislocation. METHODS: According to the Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA), MEDLINE, EMBASE, Web of Science, and PubMed were searched for results from January 1, 2000, to May 18, 2021, in two subsequent searches for English language studies that presented data on traumatic patellar dislocation. Quality of selected papers was assessed using the Methodological Index for Non-Randomised Studies (MINORS) and the Risk of Bias (RoB) 2.0 protocol. Results were qualitatively synthesised, and descriptive statistics were calculated. RESULTS: Forty studies totalling 3074 patients (1407 females) were included for the analysis. The mean age was 18.9 years (range 0-69). The population included 2446 first-time dislocations. The imaging modalities used were: 71.1% MRI, 52.6% plain radiography, 12.1% CT, and 0.68% ultrasound. In the 25 studies that reported the number of OCF, a total of 38.3% of patients were found to have OCF. 43.3% of patients with a first-time dislocation, and 34.7% of patients with previous dislocations, had at least one OCF. In the included paediatric studies (maximum age ≤ 18), the presence of OCF was detected by plain radiography in 10.1% of patients, MRI in 76.6% of patients, and CT in 89.5% of patients. For management of an OCF, the surgical options include fixation for larger pieces, excision for smaller pieces, and conservative management on a case-by-case basis. CONCLUSIONS: Based on the current available evidence, assessment and management of patellar dislocations and subsequent OCFs vary, with radiography and MRI as the main imaging modalities on presentation and particular benefit for MRI in the paediatric population. Findings from this study suggest the highest rate of OCF detection with MRI, and thus, surgeons should consider routinely ordering an MRI in patients with first-time patellar dislocation. Regarding management of OCFs, the main indication for fixation was large fragments, while smaller and poor-quality fragments are excised. Few studies choose conservative management of OCFs due to later requirements for surgical management. Future work should focus on large, high-quality studies, and implementation of randomised control trials to form guidelines for imaging patellar dislocations and management of OCFs. LEVEL OF EVIDENCE: Level IV.
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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 | 0.013 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".