WOUND MANAGEMENT, HEALING, AND EARLY PROSTHETIC REHABILITATION: PART 2 - A SCOPING REVIEW OF PHYSICAL BIOMARKERS
Bibliographic record
Abstract
BACKGROUND: The timely provision of load-bearing prostheses significantly reduces healthcare costs and lowers post-amputation mortality risk. However, current methods for assessing residuum health remain subjective, underscoring the need for standardized, evidence-based approaches incorporating physical biomarkers to evaluate residual limb healing and determine readiness for prosthetic rehabilitation. OBJECTIVE(S): This review aimed to identify predictive, diagnostic, and indicative physical biomarkers of healing of the tissues and structures found in the residual limbs of adults with amputation. METHODOLOGY: A scoping review was conducted following Joanna Briggs Institute (JBI) and PRISMA-ScR guidance. Searches using “biomarkers”, “wound healing”, and “amputation” were performed on May 6, 2023, on Web of Science, Ovid MEDLINE, Ovid Embase, Scopus, Cochrane, PubMed, and CINAHL databases. Inclusion criteria were: 1) References to physical biomarkers and healing; 2) Residuum tissue healing; 3) Clear methodology with ethical approval; 4) Published from 2017 onwards. Articles were assessed for quality (QualSyst tool) and evidence level (JBI system), and categorized by study, wound, and model type. Physical biomarkers that were repeated not just within categories, but across more than one of the study categories were reported on. FINDINGS: The search strategy identified 3,306 sources, 157 of which met the inclusion criteria. Histology was the most frequently repeated physical biomarker used in 64 sources, offering crucial diagnostic insights into cellular healing processes. Additional repeated indicative and predictive physical biomarkers, including ankle-brachial index, oxygenation measures, perfusion, and blood pulse and pressure measurements, were reported in 25, 19, 13, and 12 sources, respectively, providing valuable data on tissue oxygenation and vascular health. CONCLUSION: Ultimately, adopting a multifaceted approach that integrates a diverse array of physical biomarkers (accounting for physiological factors and comorbidities known to influence healing) may substantially enhance our understanding of the healing process and inform the development of effective rehabilitation strategies for individuals undergoing amputation. Layman's Abstract Providing prosthetic limbs soon after amputation reduces healthcare costs and lowers mortality risk. However, current methods for evaluating the health of the remaining limb often rely on subjective judgment, highlighting the need for a standardized, evidence-based approach using physical biomarkers to assess healing and readiness for prosthetics. This review aimed to identify physical biomarkers that can predict, diagnose, or indicate healing in amputated limbs. On May 6, 2023, a comprehensive review was conducted across multiple databases, including Web of Science, Ovid MEDLINE, Ovid Embase, Scopus, Cochrane, PubMed, and CINAHL, to find studies using search terms like “biomarkers”, “wound healing”, and “amputation”. To be included, studies had to focus on biomarkers related to healing in residual limbs, use clear research methods, have ethical approval, and be published after 2017. The quality of the studies was evaluated, and biomarkers found across multiple studies were reported. Of 3,306 sources identified, 157 focused on physical biomarkers, with histology (tissue analysis) being the most commonly reported, allowing healing progress to be diagnosed at the cellular level. Other frequently mentioned biomarkers included the ankle-brachial index and oxygenation measures, which are used to assess tissue oxygen levels and blood flow, therefore predicting or indicating healing. Using a combination of different physical markers (while considering things like overall health and existing medical conditions) can give us a much better understanding of how healing works. This approach can also help create more effective rehabilitation plans for people who have had an amputation. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/43716/33400 How To Cite: Williams-Reid H, Johannesson A, Buis A. Wound management, healing, and early prosthetic rehabilitation: Part 2 - A scoping review of physical biomarkers. Canadian Prosthetics & Orthotics Journal. 2024; Volume 7, Issue 2, No.3. https://doi.org/10.33137/cpoj.v7i2.43716 Corresponding Author: Professor Arjan Buis, PhDDepartment of Biomedical Engineering, Faculty of Engineering, University of Strathclyde, Glasgow, Scotland.E-Mail: arjan.buis@strath.ac.ukORCID ID: https://orcid.org/0000-0003-3947-293X
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".