Asking New Questions with Old Data: The Centralized Open-Access Rehabilitation Database for Stroke
Bibliographic record
Abstract
BACKGROUND: troke (SCOAR), which allows researchers to quickly visualize relationships among variables, efficiently share data, generate hypotheses, and enhance clinical trial design. METHODS: Bibliographic databases were searched according to inclusion criteria leaving 2,892 titles that were further screened to 514 manuscripts to be screened by full text, leaving 215 randomized controlled trials (RCTs) in the database (489 independent groups representing 12,847 patients). Demographic, methodological, and statistical data were extracted by independent coders and entered into SCOAR. RESULTS: Trial data came from 114 locations in 27 different countries and represented patients with a wide range of ages, 62 year [41; 85] [shown as median (range)] and at various stages of recovery following their stroke, 141 days [1; 3372]. There was considerable variation in the dose of therapy that patients received, 20 h [0; 221], over interventions of different durations, 28 days [10; 365]. There was also a lack of common data elements (CDEs) across trials, but this lack of CDEs was most pronounced for baseline assessments of patient impairment and severity of stroke. CONCLUSION: Data integration across hundreds of RCTs allows clinicians and researchers to quickly visualize data from the history of the field and lays the foundation for making SCOAR a living database to which researchers can upload new data as trial results are published. SCOAR is a useful tool for clinicians and researchers that will facilitate data visualization, data sharing, the finding of relevant past studies, and the design of clinical trials by enabling more accurate and comprehensive power analyses. Furthermore, these data speak to the need for CDEs specific to stroke rehabilitation in randomized controlled trials. PROSPERO 2014: CRD42014009010.
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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 | 0.038 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.035 | 0.042 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".