Optimal Pharmacological Management and Prevention of Glucocorticoid-Induced Osteoporosis (GIOP): Protocol for a Systematic Review and Network Meta-Analysis
Notice bibliographique
Résumé
ABSTRACT Introduction Glucocorticoid (GC) administration is an effective therapy commonly used in the treatment of autoimmune and inflammatory diseases. However, the use of GC can give rise to serious complications. The main detrimental side effect of GC therapy is significant bone loss, resulting in glucocorticoid-induced osteoporosis (GIOP). There are a variety of treatments available for preventing and managing GIOP; however, without clearly defined guidelines, it can be very difficult for physicians to choose the optimal therapy for their patients. Previous network meta-analyses (NMAs) and meta-analyses did not include all available RCT trials, or only performed pairwise comparisons. We present a protocol for a NMA that incorporates all available RCT patient data to provide the most comprehensive ranking of all available GIOP treatments in terms of their ability to increase bone mineral density (BMD) and decrease fracture incidences among adult patients undergoing GC treatments. Methods and Analysis We will search MEDLINE, EMBASE, PubMed, Web of Science, CINAHL, CENTRAL and Chinese literature sources (CNKI, CQVIP, Wanfang Data, Wanfang Med Online) for randomized controlled trials (RCTs) which fit our criteria. RCTs that evaluate different antiresorptive regimens taken by adult patients undergoing GC therapy during the study or had taken GC for at least 3 months in the year prior to study commencement with lumbar spine BMD, femoral neck BMD, total hip BMD, vertebral fracture incidences and/or non-vertebral fracture incidences as outcomes will be selected. We will perform title/abstract and full-text screening as well as data extraction in duplicate. Risk of bias (ROB) will be evaluated in duplicate for each study, and the quality of evidence will be examined using CINeMA in accordance to the GRADE framework. We will use R and gemtc to perform the NMA. We will report BMD results as weighted mean differences (WMDs) and standardized mean differences (SMDs), and we will report fracture incidences as odds ratios. We will use the surface under the cumulative ranking curve (SUCRA) scores to provide numerical estimations of the rankings of interventions. Ethics and Dissemination The study will not require ethical approval. The findings of the NMA will be disseminated in a peer-reviewed journal and presented at conferences. We aim to produce the most comprehensive quantitative analysis regarding the management of GIOP. Our analysis should be able to provide physicians and patients with an up-to-date recommendation for pharmacotherapies in reducing incidences of bone loss and fractures associated with GIOP. Systematic Review Registration International Prospective Register for Systematic Reviews (PROSPERO) — CRD42019127073 ARTICLE SUMMARY Strengths and limitations of this study Literature search in Chinese databases will likely yield huge amounts of new RCT evidence regarding GIOP Reporting change in BMD outcomes as standardized mean differences allow the pooling of absolute and percentage change data, increasing the number of RCT trials included Only RCTs will be included, quality of trials and networks will be evaluated using Risk of Bias and GRADE Older trials may report inaccurate results due to outdated procedures and hardware Chinese clinicians may not use the same procedures and practices as Western clinicians
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 | 0,046 | 0,093 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,003 |
| Méta-épidémiologie (sens large) | 0,024 | 0,033 |
| Bibliométrie | 0,014 | 0,014 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,005 | 0,005 |
| Intégrité de la recherche | 0,005 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,039 | 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 ».