How to personalise cognitive–behavioural therapy for chronic primary pain using network analysis: study protocol for a single-case experimental design with multiple baselines
Bibliographic record
Abstract
INTRODUCTION: Cognitive-behavioural therapy (CBT) is an effective treatment for chronic primary pain (CPP), but effect sizes are small to moderate. Process orientation, personalisation, and data-driven clinical decision-making might address the heterogeneity among persons with CPP and are thus promising pathways to enhance the effectiveness of CBT for CPP. This study protocol describes one approach to personalise CBT for CPP using network analysis. METHODS AND ANALYSIS: A single-case experimental design with multiple baselines will be combined with ecological momentary assessment (EMA). Feasibility and acceptance of the study procedure will be demonstrated on a sample of n=12 adults with CPP in an outpatient clinic. In phase A, participants complete 21 days of EMA, followed by the standard diagnostic phase of routine clinical care (phase B). Person-specific, process-based networks are estimated based on EMA data. Treatment targets are selected using mean ratings, strength and out-strength centrality. After a second, randomised baseline (phase A'), participants will receive 1 out of 10 CBT interventions, selected by an algorithm matching targets to interventions, in up to 10 sessions (phase C). Finally, another EMA phase of 21 days will be completed to estimate a post-therapy network. Tau-U and Hedges' g are used to indicate individual treatment effects. Additionally, conventional pain disability measures (Pain Disability Index and the adapted Quebec Back Pain Disability Scale) are assessed prior, post, and 3 months after phase C. ETHICS AND DISSEMINATION: Ethical considerations were made with regard to the assessment-induced burden on the participants. This proof-of-concept study may guide future studies aiming at personalisation of CBT for CPP as it outlines methodological decisions that need to be considered step by step. The project was approved by the local ethics committee of the psychology department of University Kaiserslautern-Landau (#LEK-457). Participants gave their written informed consent prior to any data assessment and app installation. The results of the project will be published, presented at congresses, and relevant data will be made openly accessible via the Open Science Framework (OSF). TRIAL REGISTRATION NUMBER: NCT06179784.
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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 | 0.046 | 0.045 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.070 | 0.015 |
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".