Characteristics, Patterns, and Optimal Treatment Strategies of Morel-Lavallee Lesions: A Systematic Review
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,007 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».