Prevention of bone loss and fractures following solid organ transplantations: Protocol for a systematic review and network meta-analysis
Bibliographic record
Abstract
ABSTRACT Purpose Solid organ transplant (SOT) recipients can develop skeletal diseases caused by underlying conditions and the use of immunosuppressants. As a result, SOT recipients are at risk for decreased bone mineral density (BMD) and increased fracture incidences. We propose a network meta-analysis (NMA) that incorporates all available RCT data to provide the most comprehensive ranking of antiresorptive interventions according to their ability to decrease fracture incidences and increase BMD in SOT recipients. Methods We will search MEDLINE, EMBASE, Web of Science, CINAHL, CENTRAL and Chinese literature sources for RCTs, and we will include adult SOT recipients who took antiresorptive therapies starting at the time of transplant with relevant outcomes. We will perform title and full-text screening as well as data extraction in duplicate. We will report changes in BMD as weighted or standardized mean differences, and fracture incidences as risk ratios. We will use SUCRA scores to provide rankings of interventions, and we will examine the quality of evidence using risk of bias and CINeMA. Results The results of this systematic review and network meta-analysis will be published in a peer-reviewed journal. Conclusions To our knowledge, this systematic review and network meta-analysis will be the most comprehensive quantitative analysis regarding the management of bone loss and fractures in SOT recipients. 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 SOT. CONFLICT OF INTEREST Jiawen Deng, and Wenteng Hou declare that they have no conflict of interest. MINI ABSTRACT We propose a network meta-analysis investigating the use of antiresorptive interventions to prevent bone loss and fractures in solid organ transplant (SOT) recipients. We aim to provide a comprehensive ranking of antiresorptive therapies in terms of their ability to increase bone mineral density and decrease fracture incidence in SOT recipients.
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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 | 0.064 | 0.099 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.051 | 0.004 |
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".