Bibliographic record
Abstract
<div><p>Background</p><p>Lower limb loss (LLL) is a distressing experience with psychological, physical, and social challenges. Education is needed to enhance the coping skills and confidence of patients to improve LLL outcomes. However, access to rehabilitation services and education is limited outside of urban centers. To address this service gap, we co-created an eHealth platform, called <b>S</b>elf-<b>M</b>anagement for <b>A</b>mputee <b>R</b>ehabilitation using <b>T</b>echnology (SMART).</p><p>Objectives</p><p>First, we will test the effect of SMART and usual care compared with usual care only on walking capacity and confidence among individuals with LLL. Second, we will describe key implementation factors for program delivery and adoption at the person- and provider-level.</p><p>Methods</p><p>This is a Type 1 Effectiveness-Implementation Hybrid Design, mixed-methods, multi-site (British Columbia and Ontario, Canada), parallel, assessor-blinded randomized controlled trial. Participants will include adults with unilateral LLL, during early prosthetic fitting (<2 years after casting for initial prosthesis). Participants in both groups will receive usual care. The experimental group will receive SMART with weekly support sessions from a trained peer mentor for goal setting and action planning for six weeks. Participants will be encouraged to continue using SMART for an additional four weeks. The control group will receive usual care, and weekly social contacts for six weeks. The primary outcome measure is walking capacity operationalized as the performance based Timed Up and Go test. The secondary outcome is walking confidence using the Ambulatory Self-Confidence Questionnaire. Outcome measures will be assessed at baseline, immediately post-intervention, and four weeks follow-up. We will describe key implementation factors (such as, participant experience, intervention adoption, fidelity) throughout the study using questionnaires, semi-structured interviews, and direct observation.</p><p>Results</p><p>No participants have been enrolled.</p><p>Conclusions</p><p>SMART has the potential to provide knowledge and skill development to augment rehabilitation outcomes for adults with LLL.</p><p>Trial registration</p><p><a href="https://clinicaltrials.gov/ct2/show/NCT04953364" target="_blank">NCT04953364</a> in Clinical Trial Registry (<a href="https://clinicaltrials.gov/" target="_blank">https://clinicaltrials.gov/</a>).</p></div>
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.012 | 0.011 |
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; both teacher heads agree on what is shown here.
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".