Characteristics, Patterns, and Optimal Treatment Strategies of Morel-Lavallee Lesions: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: To evaluate the diagnostic accuracy of imaging modalities and outcomes of treatment strategies for Morel-Lavallée lesions (MLLs) and provide evidence-based recommendations for optimal management. METHODS: Data Sources: MEDLINE, Embase, and Emcare databases were systematically searched for English-language studies published up to September 2024. STUDY SELECTION: Observational studies and randomized controlled trials (RCTs) reporting diagnostic accuracy or treatment outcomes for MLLs were included. Case reports, small series, animal studies, and non-English articles were excluded. DATA EXTRACTION: Study quality was assessed using the Methodological Index for Non-randomized Studies (MINORS) tool. Data on demographics, lesion characteristics, imaging modalities, and outcomes were extracted. Lesions were categorized as small (<100 cm3) or large (≥100 cm3) based on volume. DATA SYNTHESIS: Descriptive statistics summarized outcomes. Recurrence rates were calculated and pooled proportions compared across treatment modalities. RESULTS: Twenty-nine studies (928 patients, 964 lesions) were included. MLLs most frequently occurred in the thigh (26.5%), greater trochanter (24.9%), and lumbar region (20.3%). Among smaller lesions (<100 cm3), nonoperative treatment had a low recurrence rate (5.6%), while for larger lesions (>100 cm3), percutaneous management was associated with the highest recurrence rate (15%) compared to other treatment approaches. Operative treatment of large lesions had a 50% recurrence rate in one study, while sclerodesis achieved the lowest rate (4.8%) for lesions averaging 387 cm3, however, this finding is based on a limited number of cases (21 lesions). MRI was the most common single imaging modality reported (n=162 lesions, 19.5%), favoured for its superior soft-tissue characterization. Ultrasound was used in 121 lesions (14.6%) as an accessible initial assessment tool, while CT, often performed incidentally during trauma evaluation, diagnosed 339 lesions (40.9%). CONCLUSIONS: MRI was the most used single modality for diagnosing MLLs. Small, acute lesions were effectively managed nonoperatively. Large lesions (>100 cm3) often required operative management. Sclerodesis appears promising with the lowest recurrence (4.8%), but further studies are needed. Standardized treatment protocols may help improve outcomes and reduce recurrence. LEVEL OF EVIDENCE: IV, systematic review.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| 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 | 0.002 | 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".