Improving the development, monitoring and reporting of stroke rehabilitation research: Consensus-based core recommendations from the Stroke Recovery and Rehabilitation Roundtable
Bibliographic record
Abstract
Recent reviews have demonstrated that the quality of stroke rehabilitation research has continued to improve over the last four decades but despite this progress, there are still many barriers in moving the field forward. Rigorous development, monitoring and complete reporting of interventions in stroke trials are essential in providing rehabilitation evidence that is robust, meaningful and implementable. An international partnership of stroke rehabilitation experts committed to develop consensus-based core recommendations with a remit of addressing the issues identified as limiting stroke rehabilitation research in the areas of developing, monitoring and reporting stroke rehabilitation interventions. Work exploring each of the three areas took place via multiple teleconferences and a two-day meeting in Philadelphia in May 2016. A total of 15 recommendations were made. To validate the need for the recommendations, the group reviewed all stroke rehabilitation trials published in 2015 (n = 182 papers). Our review highlighted that the majority of publications did not clearly describe how interventions were developed or monitored during the trial. In particular, under-reporting of the theoretical rationale for the intervention and the components of the intervention call into question many interventions that have been evaluated for efficacy. More trials were found to have addressed the reporting of interventions recommendations than those related to development or monitoring. Nonetheless, the majority of reporting recommendations were still not adequately described. To progress the field of stroke rehabilitation research and to ensure stroke patients receive optimal evidence-based clinical care, we urge the research community to endorse and adopt our recommendations.
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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 | 0.444 | 0.654 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.023 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.020 | 0.032 |
| Open science | 0.019 | 0.019 |
| Research integrity | 0.038 | 0.040 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".