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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| 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".