Optimal Pharmacological Management and Prevention of Glucocorticoid-Induced Osteoporosis (GIOP): Protocol for a Systematic Review and Network Meta-Analysis
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.046 | 0.093 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.024 | 0.033 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".