What is the current state of precision rehabilitation? Protocol for a scoping study with a consultation phase
Bibliographic record
Abstract
INTRODUCTION: Precision health can be described as the right intervention, at the right time, for the right person, with a focus on monitoring and maintaining health in a longitudinal approach. Despite an increasing focus on precision approaches in medicine, their application in a rehabilitation context remains unexplored. As such, a greater understanding of the current state of the literature is required, in combination with clinician, researcher and healthcare manager perspectives regarding barriers and facilitators to the practical implementation of precision rehabilitation in clinical practice. OBJECTIVE: Describe and map the current state of knowledge regarding precision rehabilitation to identify gaps in knowledge and inform future research directions and clinical implementation strategies. METHODS AND ANALYSIS: , 2021) and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Scoping Review Extension guidelines. A convergent mixed-methods design will combine quantitative and qualitative findings. A search in Medline, CINAHL, Embase, Scopus, Web of Science and PsycINFO databases will be conducted for articles published between 2010 and 2023 referring to the concept of precision rehabilitation. Two reviewers will complete an abstract and full-text review based on eligibility criteria; data will be extracted from accepted papers using a data extraction framework. Results will be aggregated and synthesised using descriptive and thematic analyses. The consultation phase will involve a purposeful sampling of key stakeholders (clinicians, researchers and managers) in large North American rehabilitation centres. Semi-structured individual interviews will be conducted and analysed using deductive thematic analysis. Convergent mixed-methods data analyses will combine quantitative and qualitative datasets to highlight similarities and differences between the current literature on the subject and the understanding of stakeholders. ETHICS AND DISSEMINATION: Ethical approval has been obtained from the Research Ethics Board of the Sainte-Justine University Health Centre (no. 2024-6324). Results will be disseminated through professional networks, conference presentations and publications in scientific journals.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.145 | 0.142 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.016 | 0.012 |
| Insufficient payload (model declined to judge) | 0.096 | 0.026 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".