Incidence, Prediction, and Prevention of Fractures After Kidney Transplantation: A Systematic Review Protocol
Bibliographic record
Abstract
Background: Kidney transplant recipients are uniquely exposed to the disordered bone metabolism associated with chronic kidney disease beginning before transplantation followed by chronic corticosteroid use after transplantation. Previous efforts to synthesize the rapidly accruing evidence regarding estimation and management of fracture risk in kidney transplant recipients are outdated and incomplete. Objective: To synthesize the evidence informing the overall incidence, patient-specific risk prediction, and methods of prevention of fractures in patient living with a kidney transplant. Design: Three systematic reviews will address the following questions: What is the overall incidence of skeletal fracture after kidney transplantation (review 1)? Which prediction models and individual prognostic factors predict fracture in kidney transplant recipients (review 2)? and How effective are different antifracture interventions at preventing fracture or improving surrogate markers of bone health in kidney transplant recipients (review 3)? Setting: Cohort studies (reviews 1 and 2) and randomized trials (review 2) with a mean/median follow-up ≥12 months beginning after transplant. Review 3: randomized trials or new-user cohort studies with concurrent controls evaluating the effect of antifracture interventions including bisphosphonates, calcium supplementation, cinacalcet, denosumab, parathyroid hormone analogues, parathyroidectomy, raloxifene, romosozumab, steroid withdrawal or minimization protocols after kidney transplant, vitamin D (both active and nutritional), other antifracture interventions. Patients: Adult kidney transplant recipients in studies published after the year 2000. Measurements: Review 1: incidence rate or cumulative risk of fracture. Review 2: For prediction models, measures of discrimination (eg, c-statistic), calibration (calibration curves, observed:expected ratios), and net benefit (ie, from decision curve analysis); for individual prognostic factors, relative measures of association with fractures. Review 3: measures of treatment effect on fractures and on surrogate markers of bone health (eg, bone mineral density, trabecular bone score). Methods: We searched MEDLINE, Embase, and the Cochrane Library using subject headings and keywords related to kidney transplant and fractures. Pairs of reviewers will screen records independently in duplicate to identify studies relevant to one or more of the 3 reviews and categorize each study accordingly. Single reviewers will extract data and evaluate risk of bias for each included study using one of the following tools as appropriate: the Quality of Prognostic Studies tool, the Prediction model Risk Of Bias ASsessment tool, the Risk Of Bias In Non-randomised Studies-of Interventions tool, and the Cochrane Risk of Bias 2.0 tool. A second reviewer will independently verify. We will synthesize study-level summary estimates by random-effects meta-analysis for review 1, by vote counting and random-effects meta-analysis in review 2, and by random effects pairwise and, if feasible, network meta-analysis in review 3. We will summarize findings according to latest guidance of the Grading of Recommendations Assessment, Development, and Evaluation Working Group applicable to each review. Limitations: Reliance on published studies is susceptible to publication bias, particularly in studies of prediction (review 2) and of treatment effects (review 3). Conclusions: This review will provide an evidence update on 3 topics of relevance to patients, clinicians, guideline developers, and researchers.
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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 | 0.028 | 0.048 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.020 | 0.019 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.046 | 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".