Consensus-Based Recommendations for Comprehensive Clinical Assessment in Prosthetic Care: A Delphi Study
Bibliographic record
Abstract
Background/Objective: The most effective strategy for addressing users’ prosthetic needs is a comprehensive clinical assessment that provides a holistic understanding of the individual’s symptoms, health, function, and environmental barriers and facilitators. A standardized evaluation form would provide guidance for a structured approach to comprehensive clinical assessments of people with LLA. The objective of this study was to determine a list of relevant elements to be included in prosthetic evaluation for adults with lower limb amputation. Methods: Three independent focus group discussions were conducted with prosthetists (n = 15), prosthesis users (n = 11), and decision makers (n = 4) to identify all relevant elements that should be included in the clinical assessment of prosthetic services. The final content was then determined using the Delphi technique, with 35 panelists (18 prosthetists and decision makers, and 17 prosthesis users) voting in each round. Results: A total of 91 elements were identified through the focus group, of which 78 were included through the Delphi process. The identified elements are mostly related to the physical health of the prosthesis user (e.g., mobility, pain, and medical information), while others address personal or psychosocial aspects (e.g., activities of daily living, goals, and motivation) or technical aspects (prosthesis-related). Conclusions: Through a Delphi consensus, a list of relevant elements to be included in a prosthetic evaluation was generated. These results will inform the development of a standardized clinical prosthetic assessment form. This form has the potential to improve the quality of clinical evaluations, guide interventions, and enhance the well-being of prosthetic users.
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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 | 0.477 | 0.389 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".