Single-Case Experimental Design in Rehabilitation
Bibliographic record
Abstract
ABSTRACT: Single-case experimental design is a family of experimental methods that can be used to examine the efficacy of interventions by testing a small number of patients or cases. This article provides an overview of single-case experimental design research for use in rehabilitation as another option along with traditional group-based research when studying rare cases and rehabilitation interventions of unknown efficacy. Basic concepts related to single-case experimental design and the characteristics of common subtypes ( N-of-1 randomized controlled trial, withdrawal design, multiple-baseline design, multiple-treatment design, changing criterion/intensity design, and alternating treatment design) are introduced. The advantages and disadvantages of each subtype are discussed along with challenges in data analysis and interpretation. Criteria and caveats for interpreting single-case experimental design results and their use in evidence-based practice decisions are discussed. Recommendations are provided for appraising single-case experimental design articles as well as using single-case experimental design principles to improve real-world clinical evaluation.
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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 | 0.304 | 0.405 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".