Evaluating the Reliability of a Shape Capturing Process for Transradial Residual Limb Using a Non-Contact Scanner
Bibliographic record
Abstract
Advancements in digital imaging technologies hold the potential to transform prosthetic and orthotic practices. Non-contact optical scanners can capture the shape of the residual limb quickly, accurately, and reliably. However, their suitability in clinical practice, particularly for the transradial (below-elbow) residual limb, is unknown. This project aimed to evaluate the reliability of an optical scanner-based shape capture process for transradial residual limbs related to volumetric measurements and shape assessment in a clinical setting. A dedicated setup for digitally shape capturing transradial residual limbs was developed, addressing challenges with scanning of small residual limb size and aspects such as positioning and patient movement. Two observers performed three measurements each on 15 participants with transradial-level limb absence. Overall, the developed shape capture process was found to be highly repeatable, with excellent intra- and inter-rater reliability that was comparable to the scanning of residual limb cast models. Future work in this area should compare the differences between residual limb shapes captured through digital and manual methods.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".