Techniques for Measuring the Fluctuation of Residual Lower Limb Volume in Clinical Practices: A Systematic Review of the Past Four Decades
Bibliographic record
Abstract
Increased pressure and shear stress distributions at the limb–socket interface are hypothesised to result from changes in the residual limb shape and volume, which can cause socket fit difficulties. Accurate residual limb volume measurements may aid clinicians in developing strategies to accommodate volume fluctuations. This review primarily aims to analyse the techniques available for measuring the residual lower limb volume that may be used in clinical settings, as documented in the works published over the previous four decades. A comprehensive search of articles in PubMed, ScienceDirect, Web of Science, and Google Scholar identified 904 articles, and further analysis resulted in only 39 articles being chosen to be analysed. Based on the findings, there are nine techniques available to measure the residual limb volume: water displacement, anthropometric measurement, contact probes, optical scanning, spiral X-ray computed tomography (SXCT), magnetic resonance imaging (MRI), ultrasound, laser scanning, and bioimpedance. Considering the variety of techniques for determining residual limb volume, it is critical to choose the ones that best suit clinicians’ objectives, and each technique has potential sources of error that should be avoided by taking precautionary action. A comprehensive study of the measurement techniques is needed since researchers have developed and extensively utilised many new measuring devices, especially handheld 3D laser scanners.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".