Lung transplantation and bone health: A narrative review
Bibliographic record
Abstract
Bone health after lung transplantation has not been comprehensively reviewed in over 2 decades. This narrative review summarizes the available literature on bone health in the context of lung transplantation, including epidemiology, presentation, and postoperative management. Osteoporosis is reported in approximately 30% to 50% of lung transplant candidates, largely due to disease-related impact on bone and lifestyle, and corticosteroid-related effects during end-stage lung disease (interstitial lung diseases, chronic obstructive pulmonary disease, and historically cystic fibrosis). After lung transplantation, many patients experience steroid-induced bone loss, followed by stabilization or recovery to baseline levels with pharmacologic management. Although evidence on fracture incidence is limited, fracture risk appears to increase in the year following transplantation, with common fracture sites including the vertebrae and the ribs. Vertebral and rib fractures restrict chest expansion and affect lung function, underscoring the importance of fracture prevention in lung transplant recipients. There is limited evidence on the pharmacologic management of osteoporosis after lung transplantation. Existing randomized controlled trials have focused on parenteral bisphosphonates and calcitriol but have been underpowered to evaluate their effect on fracture outcomes. Resistance training, particularly in conjunction with antiresorptive therapy, has also been shown to improve bone health when initiated 2 months after transplantation. No studies to date have documented the effectiveness of denosumab in lung transplant recipients; more studies on pharmacotherapy are warranted to elucidate optimal medical management. Considering the high osteoporosis prevalence and fracture risk in lung transplant populations, the development of formal guidance is warranted to promote improved management after transplantation.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".