ALIGN: Advancing Learning in Neurorehabilitation
Bibliographic record
Abstract
This scoping review will support the development of a patient-centred measurement (PCM) framework for neurorehabilitation within Alberta’s Learning Health System (LHS). The framework will guide the standardized collection of outcome measures across stroke, traumatic brain injury (TBI), and spinal cord injury (SCI) populations to inform precision rehabilitation and continuous learning from clinical practice. A key barrier to implementing such a framework is that existing core outcome measure sets for these populations are typically designed for research purposes rather than for routine clinical workflows. Moreover, these sets are not integrated across conditions, resulting in a fragmented measurement landscape for clinicians. To address this gap, we will conduct a scoping review to synthesize clinical and patient-reported outcome measures recommended for use in neurorehabilitation for stroke, TBI, and SCI. The results will provide a consolidated view of existing recommended measures and inform the co-design of an outcome measurement framework tailored for real-world clinical use. This work is a foundational step toward improving patient-centred care, supporting precision rehabilitation, and enabling continuous learning within Alberta’s neurorehabilitation services. Expected outcomes include a comprehensive summary of recommended outcome measures and practical insights to guide their integration into clinical practice.
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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 | 0.105 | 0.148 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".