An Overview of the Methodology Used to Develop Clinical Practice Guidelines for the Management of Acute and Intraoperative Spinal Cord Injury
Notice bibliographique
Résumé
STUDY DESIGN: An overview of the methods used to develop clinical practice guidelines (CPGs). OBJECTIVES: Acute spinal cord injury (SCI) and intraoperative SCI (ISCI) can have devastating physical and psychological consequences for patients and their families. To date, there are several studies that have discussed the diagnostic and management strategies for both SCI and ISCI. CPGs in SCI help to distill and translate the current evidence into actionable recommendations, standardize care across centers, optimize patient outcomes, and reduce costs and unnecessary interventions. Furthermore, they can be used by patients to assist in making decisions about certain treatments and by policy makers to inform allocation of resources. The objective of this article is to summarize the methods used to develop CPGs for the timing of surgery and hemodynamic management of acute SCI, as well as the identification and treatment of ISCI. METHODS: The CPGs were developed using standards established by the Institute of Medicine (now the National Academy of Medicine), the Guideline International Network and several other organizations. Systematic reviews were conducted according to accepted methodological standards (eg, Institute of Medicine, Agency for Healthcare Research and Quality and Patient-Centered Outcomes Research Institute) in order to summarize the current body of evidence and inform the guideline development process. Protocols for each guideline were created. A multidisciplinary guideline development group (GDG) was formed that included individuals living with SCI as well as clinicians from the broad range of specialties that encounter patients with SCI: spine or trauma surgeons, critical care physicians, rehabilitation specialists, neurologists, anesthesiologists and other healthcare professionals. Individuals living with SCI were also included in the GDG. The Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach was used to rate the certainty of the evidence for each critical outcome. The "evidence to recommendation" framework was then used to translate the evidence obtained from the systematic review to an actionable recommendation. This framework provides structure when assessing the body of evidence and considers several additional factors when rating the strength of the recommendation, including the magnitude of benefits and harms, patient preferences, resource use, health equities, acceptability and feasibility. Finally, the CPGs were appraised both internally and externally. RESULTS: The results of the CPGs for SCI are provided in separate articles in this focus issue. CONCLUSIONS: Development of these CPGs for SCI followed the methodology proposed by the Institute of Medicine the Guideline International Network and the GRADE Working Group. It is anticipated that these CPGs will assist clinicians implement the best evidence into practice and facilitate shared-decision making with patients.
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 | 0,312 | 0,415 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,003 |
| Méta-épidémiologie (sens large) | 0,004 | 0,008 |
| Bibliométrie | 0,027 | 0,025 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,010 | 0,005 |
| Science ouverte | 0,006 | 0,008 |
| Intégrité de la recherche | 0,004 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,003 |
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 ».