Advancing Stroke Recovery Through Improved Articulation of Nonpharmacological Intervention Dose
Bibliographic record
Abstract
Dose articulation is a universal issue of intervention development and testing. In stroke recovery, dose of a nonpharmaceutical intervention appears to influence outcome but is often poorly reported. The challenges of articulating dose in nonpharmacological stroke recovery research include: (1) the absence of specific internationally agreed dose reporting guidelines; (2) inadequate conceptualization of dose, which is multidimensional; and (3) unclear and inconsistent terminology that incorporates the multiple dose dimensions. To address these challenges, we need a well-conceptualized and consistent approach to dose articulation that can be applied across stroke recovery domains to stimulate critical thinking about dose during intervention development, as well as promote reporting of planned intervention dose versus actually delivered dose. We followed the Design Research Paradigm to develop a framework that guides how to articulate dose, conceptualizes the multidimensional nature and systemic linkages between dose dimensions, and provides reference terminology for the field. Our framework recognizes that dose is multidimensional and comprised of a duration of days that contain individual sessions and episodes that can be active (time on task) or inactive (time off task), and each individual episode can be made up of information about length, intensity, and difficulty. Clinical utility of this framework was demonstrated via hypothetical application to preclinical and clinical domains of stroke recovery. The suitability of the framework to address dose articulation challenges was confirmed with an international expert advisory group. This novel framework provides a pathway for better articulation of nonpharmacological dose that will enable transparent and accurate description, implementation, monitoring, and reporting, in stroke recovery research.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".