Systematic Search and Modified e-Delphi Consensus for Serum Bone Biomarkers in Humans and Animal Models with SCI: Methodology
Bibliographic record
Abstract
Introduction: Alterations to bone metabolism deteriorations in bone density and architecture after spinal cord injury (SCI) are complex and multifactorial: mechanical unloading, impaired osteoblast activity, altered hormone levels, and regional blood flow combine to increase lower extremity fracture incidence and mortality. Bone biomarkers are vital to detect disease, identify candidate therapies, monitor therapy effectiveness, and quantify fracture risk. Objectives: This study aimed to synthesize available literature on serum and plasma bone biomarkers in both animal and human SCI models and to generate consensus regarding their appropriateness for use across the translational continuum. Methods: A systematic search was conducted; 4731 studies were excluded, yielding 125 studies for data extraction. Data were reviewed by an interdisciplinary panel of experts. Through a modified e-Delphi process, consensus statements were iteratively developed regarding the appropriateness of 14 serum bone biomarkers in human and animal models and across the translational continuum. Results: The consensus process highlighted challenges in interpreting animal and human models, emphasizing the need for methodological rigor and standardized biomarker reporting. Consideration of diurnal variations in biomarkers and model selection (transection vs. clip) underscored the complexity of SCI research. Limitations included defining "adult" rodents and lack of data on sex-related differences in biomarkers and their interpretation, given most human data were obtained from males and animal data from females. Conclusion: The consensus statements provide guidance, address gaps in reporting and interpretation of biomarkers, promote use of standardized protocols and assay kits, and emphasize interdisciplinary approaches to advancing scientific discovery and facilitating knowledge translation.
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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 | 0.286 | 0.363 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.027 | 0.017 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".