Criteria for Advanced Prosthetic Foot Prescription: Rationale, Design, and Protocol for a Multisite, Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: The prescription of prosthetic ankle-foot devices is often based on the professional judgment of the limb loss care team or limited evidentiary research. Current prosthetic research efforts have focused on the design and development of prosthetic devices rather than on understanding which devices are the most appropriate to prescribe. This investigation will evaluate biomechanical, functional, and subjective outcome measures to help determine the optimal prescription parameters of prosthetic ankle-foot devices. OBJECTIVE: This study aims to develop evidence-based guidelines for limb loss care teams for the appropriate prescription of commercially available prosthetic ankle-foot devices to improve function and satisfaction. METHODS: This investigation will be a multisite, randomized, crossover clinical trial targeting the enrollment of 100 participants. Participants will use 3 different types of prosthetic devices (energy storing and returning, articulating, and powered) in random order. Participants will be fitted and trained with each device and then separately use each device for a 1-week acclimation period. Following each 1-week acclimation period, participants will be evaluated using several functional measures and subjective surveys. A random subset of participants (30/100, 30%) will also undergo full-body gait analysis, following each 1-week acclimation period, to collect biomechanical data during level ground and incline and decline walking. After all individual device evaluations, participants will be given all 3 prostheses concurrently for 4 weeks of home and community use to capture user preference. Activity monitoring and a guided interview will be used to determine overall user preference. RESULTS: The study was funded in August 2017, and data collection began in 2018. Data collection is expected to be completed before July 2023. Initial dissemination of results is expected to occur in the winter of 2023. CONCLUSIONS: By identifying biomechanical, functional, and subjective outcomes that are sensitive to differences in prosthetic ankle-foot devices, a benchmark of evidence can be developed to guide effective prosthetic prescription. TRIAL REGISTRATION: ClinicalTrials.gov NCT03505983; https://clinicaltrials.gov/ct2/show/NCT03505983. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/45612.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".