Notice bibliographique
Résumé
Commentary Musculoskeletal procedures performed for arthritis and other degenerative conditions can greatly improve pain, function, and quality of life. However, some patients experience major complications, with musculoskeletal infection being among the most devastating. Infection places a substantial burden on both patients and health-care systems globally, and there is universal interest in reaching consensus on the best practices for prevention, diagnosis, and treatment. Infection management is often challenging, requiring repeat surgical procedures, prolonged hospitalizations, and extended antimicrobial therapy with a substantial risk of treatment failure. Moreover, the management of suspected and/or established musculoskeletal infection remains controversial, varies by anatomic region, and lacks an accepted gold standard for diagnosis and treatment in the preoperative, intraoperative, and postoperative periods. Treatment selection remains highly variable globally and is influenced by patient factors, surgeon preference, and institutional and regional practices. Achieving international consensus on approaches to the prevention, diagnosis, and treatment of infection is crucial for optimizing patient care. In response to this tremendous need, the 3rd International Consensus Meeting (ICM) on Infection was held in Istanbul, Türkiye, from May 8 to 10, 2025, and included 857 delegates from >100 countries, with >300 questions divided across General, Hip and Knee, Biofilm, Shoulder, and Spine subsections. The article by the International Consensus Meeting Executive Committee presents an overview of the methodology behind the ICM and a summary and analysis of the top 10 questions asked that had the highest quality of evidence available to answer them. The authors report on the iterative Delphi process that was used to identify the >300 questions, which were ranked in order of importance; the answers to those questions, which were obtained using a standardized process for evidence synthesis; and the level of consensus on the findings for each question. The top 10 questions with the highest quality of evidence available to answer them were mainly related to intraoperative management. Despite having the best evidence, the answers to these questions had a wide range of consensus, ranging from low/no consensus to unanimity. Only 4 of the 10 questions had a unanimous response, and, of those, only 1 had findings showing a clear effect in reducing the risk of infection. For that question, the consensus was to strongly recommend cephalosporins, particularly cefazolin, for first-line prophylaxis in primary arthroplasty. This recommendation was based on high-quality evidence showing a significant reduction in infection risk and minimal adverse effects. One of the key strengths of this work is the rigorous >2-year process leading up to the ICM. A major contribution of this work is the finding that there is a wide range of consensus around infection prevention and management, even when the evidence is of higher quality. As highlighted by the authors, a renewed focus on knowledge translation to improve familiarity with the available evidence and to break down barriers to implementation is required to improve patient care, as well as efforts that take into account local contextual and resource-based limitations. Such efforts will be critically important when trying to apply recommendations globally, particularly when a great deal of the higher-quality evidence has been created in idealized settings. There are several limitations that should be noted when reviewing this work. First, the authors present the top 10 questions supported by the most high-quality evidence, but it is unclear how they were able to rank or determine the questions that had the best evidence. Second, it is unclear how the cutoffs for the levels of consensus were obtained. Third, for the general questions related to the overall management of infection, it is unclear how much of the evidence was derived from specific anatomic regions and whether the evidence was as strong for other anatomic regions. In conclusion, the work of the International Consensus Meeting Executive Committee highlights the fact that infection continues to be one of the biggest problems in orthopaedic surgery today, with profound and long-lasting impacts on patients, the health-care system, and society. The consensus gained from the ICM will undoubtedly improve patient care and lead to changes in surgical practice but emphasizes the need for a robust knowledge-translation strategy to maximize the potential gains. There is also an unmet need for much better evidence, as many of the critical questions in this area are still unanswered.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,114 | 0,412 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,004 | 0,009 |
| Bibliométrie | 0,016 | 0,011 |
| Études des sciences et des technologies | 0,005 | 0,006 |
| Communication savante | 0,015 | 0,016 |
| Science ouverte | 0,011 | 0,014 |
| Intégrité de la recherche | 0,031 | 0,036 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,037 | 0,016 |
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 source (Gemma direct ou Codex distillé), 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 ».