The role of single case experimental designs in evidence creation in rehabilitation
Bibliographic record
Abstract
Randomized controlled trials (RCTs) are considered the gold standard of evidence guiding intervention selection in rehabilitation. However, conduct of sufficiently powered RCTs in rehabilitation can be expensive, pose ethical and attrition concerns when participants are assigned to ineffective treatment as usual conditions, and are infeasible with low-incidence populations. Single-case experimental designs (SCEDs), including N-of-1 RCTs are causal inference studies for small numbers of participants and not necessarily one participant as the name implies. These designs are increasingly used to evaluate the effectiveness of rehabilitation interventions in diverse clinical settings and employ design features including but not limited to randomization and each participant serving as their own control. These and other internal validity enhancements can increase the confidence in study results coming from these designs. This manuscript discusses the expanded application of SCEDs in rehabilitation contexts to answer everyday clinical rehabilitation research questions with emphasis on strategies to use: 1) to maximize internal validity of this family of designs; 2) improve utility, effectiveness, and acceptability of these designs for rehabilitation end-users (clinicians, policymakers, and participants); 3) build evidence bases in areas of rehabilitation where RCTs are uncommonly used. Primary considerations for situating SCEDs within the continuum of experimental designs include increasing internal validity within designs, improving transparency in conduct and reporting of these studies, and increasing access to advanced research methods training for rehabilitation professionals.
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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 | 0.895 | 0.939 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.018 | 0.011 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.008 | 0.044 |
| Scholarly communication | 0.026 | 0.031 |
| Open science | 0.013 | 0.025 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".