Bibliographic record
Abstract
The aim of this paper is to acquaint pain researchers and practitioners with recent developments in the single-case experimental approach and their potential to allow for tailoring the treatment and its evaluation to the specific complaints, aptitudes, or profile of the individual patient, without violating the canons of good science and practice. After contrasting the single-case experimental approach and the case-study approach, we show the possibilities of customization in design, measurement, and test statistics. This is done by distinguishing 2 types of single-case designs--alternation designs and phase designs--and 2 types of replication strategies--simultaneous replications and sequential replications. In addition, tailor-made randomization tests are proposed for alternation, phase, and simultaneous replication designs and the combining of P values to perform a meta-analysis on designs that are sequentially replicated. With our emphasis on: 1) randomization in the design; 2) the possibilities for a statistical test (together with the determination of power and the calculation of effect sizes); 3) the importance of reliable and valid measurement; and 4) the role of replication, we demonstrate how internal validity, statistical-conclusion validity, construct validity, and external validity concerns can be dealt with within a single-case experimental approach framework. Finally, the many research examples and references to clinical work illustrate the usefulness of the approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".