Bibliographic record
Abstract
Over the next two decades, the number of people living with stroke in Canada is expected to rise significantly-from 405,000 in 2013 to an estimated 726,000 in 2038-as a result of aging, reduced stroke mortality, and population growth. 2 Although stroke mortality is decreasing, stroke survivors are living with disabilities; 2, 3 40% of them will have moderate to severe impairments, 4 and as much as 40% of them will have limited to no walking ability.5 Thus, the recovery of functional walking capacity is a primary goal of post-stroke survivors as well as neurorehabilitation therapists.6 The past 3 decades have seen several technological advancements in the rehabilitation of locomotion with the introduction of body weight-supported treadmill training, 7 robotic devices such as the Lokomat, 6 and human augmentation through exoskeleton devices.8 Simultaneously, virtual reality (VR)-the ability to simulate real-world objects and events-and virtual environments-providing interactive, context-specific environments simulating everyday events-have been developed as rehabilitation tools, 9 enabling the manipulation of sensory-motor experiences in a safe and progressive environment.Richards and colleagues 1 have developed a novel rehabilitation tool consisting of a VR-coupled treadmill system that provides the person post-stroke with a training environment that simulates both the visual and the physical demands of a real-life complex environment, simultaneously challenging the sensorymotor and cognitive components of locomotion.The decision to investigate this novel intervention with a person 32 months post-stroke is a welcome addition to the evidence base, which is limited for the population with chronic stroke, 10 and it demonstrates clinical improvements that identify that ongoing recovery is possible in this population.Unfortunately, access to neuro-rehabilitation is scarce for community-dwelling chronic stroke survivors.This proof-of-principle study highlights the many challenges inherent in neuro-rehabilitation research-in particular, the interface with clinical practice.The research design was creative and thorough, using a factorial case study design.The participant was a community-dwelling independent ambulator with no significant cognitive, perceptual, or communication difficulties or other comorbidities that affected his mobility.This clinical presentation will likely be difficult to replicate with larger numbers of post-stroke participants because approximately 50%-70% of stroke survivors present with cognitive deficits.11 In addition, the expanding aging population in Canada brings clinical complexity because of the increased likelihood of the coexistence of two or more chronic conditions.12 A major challenge for all health care providers is managing multi-morbidity in their scope of practice.12 Likewise, access to and use of expensive, complex technologies requires careful consideration of access to capital and training investments.Understanding the facilitators of, and barriers to, the use of complex technology in clinical practice is essential.13 Time is critical in clinical practice, and, therefore, the challenge for the clinician is weighing the training time required to use the technology effectively and safely, and the time required for appropriate set-up, along with the potential benefits and risks of using
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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 | 0.004 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.049 | 0.045 |
| Insufficient payload (model declined to judge) | 0.025 | 0.021 |
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".