Does This Patient With Diabetes Have Osteomyelitis of the Lower Extremity?
Notice bibliographique
Résumé
CONTEXT: Osteomyelitis of the lower extremity is a commonly encountered problem in patients with diabetes and is an important cause of amputation and admission to the hospital. The diagnosis of lower limb osteomyelitis in patients with diabetes remains a challenge. OBJECTIVE: To determine the accuracy of historical features, physical examination, and laboratory and basic radiographic testing. We searched for systematic reviews of magnetic resonance imaging (MRI) in the diagnosis of lower extremity osteomyelitis in patients with diabetes to compare its performance with the reference standard. DATA SOURCES: MEDLINE search of English-language articles published between 1966 and March 2007 related to osteomyelitis in patients with diabetes. Additional articles were identified through a hand search of references from retrieved articles, previous reviews, and polling experts. STUDY SELECTION: Original studies were selected if they (1) described historical features, physical examination, laboratory investigations, or plain radiograph in the diagnosis of lower extremity osteomyelitis in patients with diabetes mellitus, (2) data could be extracted to construct 2 x 2 tables or had reported operating characteristics of the diagnostic measure, and (3) the diagnostic test was compared with a reference standard. Of 279 articles retrieved, 21 form the basis of this review. Data from a single high-quality meta-analysis were used to summarize the diagnostic characteristics of MRI in osteomyelitis. DATA EXTRACTION: Two authors independently assigned each study a quality grade using previously published criteria and abstracted operating characteristic data using a standardized instrument. DATA SYNTHESIS: The gold standard for diagnosis is bone biopsy. No studies were identified that addressed the utility of the history in the diagnosis of osteomyelitis. An ulcer area larger than 2 cm2 (positive likelihood ratio [LR], 7.2; 95% confidence interval [CI], 1.1-49; negative LR, 0.48; 95% CI, 0.31-0.76) and a positive "probe-to-bone" test result (summary positive LR, 6.4; 95% CI, 3.6-11; negative LR, 0.39; 95% CI, 0.20-0.76) were the best clinical findings. A erythrocyte sedimentation rate of more than 70 mm/h increases the probability of a diagnosis of osteomyelitis (summary LR, 11; 95% CI, 1.6-79). An abnormal plain radiograph doubles the odds of osteomyelitis (summary LR, 2.3; 95% CI, 1.6-3.3). A positive MRI result increases the likelihood of osteomyelitis (summary LR, 3.8; 95% CI, 2.5-5.8). However, a normal MRI result makes osteomyelitis much less likely (summary LR, 0.14; 95% CI, 0.08-0.26). The overall accuracy (ie, the weighted average of the sensitivity and specificity) of the MRI is 89% (95% CI, 83.0%-94.5%). CONCLUSIONS: An ulcer area larger than 2 cm2, a positive probe-to-bone test result, an erythrocyte sedimentation rate of more than 70 mm/h, and an abnormal plain radiograph result are helpful in diagnosing the presence of lower extremity osteomyelitis in patients with diabetes. A negative MRI result makes the diagnosis much less likely when all of these findings are absent. No single historical feature or physical examination reliably excludes osteomyelitis. The diagnostic utility of a combination of findings is unknown.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| 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 ».