Implementation of a randomized controlled trial on an inpatient stroke rehabilitation unit: Lessons learned
Bibliographic record
Abstract
BACKGROUND/AIMS: The objective of this manuscript is to present challenges and solutions that arose during a mid-sized single-site RCT of a rehabilitation intervention performed in an inpatient stroke rehabilitation setting. METHODS: Seventy-six participants from an inpatient stroke rehabilitation unit were randomized to experimental and control groups. All participants did 30-45 min of virtual reality (VR) daily for 10-12 sessions. The experimental group did VR targeting sitting balance while the control group did VR with limited arm movement. Challenges during the implementation of the RCT were documented and strategies to mitigate them were applied. RESULTS: Challenges were placed into five categories:1. Recruitment. Our recruitment procedures required multiple steps prior to initiating direct patient contact; one solution would be to have patients consent to be approached about research upon admission to the inpatient unit.2. Patient-specific Issues. Fatigue, pain, vision problems and engagement were managed through scheduling, increasing the workload slowly and personalized modifications to the VR.3./4. Scheduling and Staffing. Recruitment and attendance at VR sessions were maximized through good communication, flexibility and cooperation, between research staff, clinical staff, volunteers, students and participants.5. Technology. Because hospital internet service was poor, a mobile internet data plan was purchased to ensure the system's reliability. CONCLUSIONS: We have identified challenges in delivering a rehabilitation intervention on an inpatient stroke rehabilitation unit and some of the measures taken to surmount these challenges. Through good planning, flexibility and collaboration, almost all of the challenges were successfully addressed. CLINICAL TRIAL REGISTRATION NUMBER: URL: http://www.clinicaltrials.gov. Unique identifier: NCT02285933.
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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 | 0.078 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".