Developing a core outcome domain set to assessing effectiveness of interdisciplinary multimodal pain therapy: the VAPAIN consensus statement on core outcome domains
Bibliographic record
Abstract
Interdisciplinary multimodal pain therapy (IMPT) is a biopsychosocial treatment approach for patients with chronic pain that comprises at least psychological and physiotherapeutic interventions. Core outcome sets (COSs) are currently developed in different medical fields to standardize and improve the selection of outcome domains, and measurement instruments in clinical trials, to make trial results meaningful, to pool trial results, and to allow indirect comparison between interventions. The objective of this study was to develop a COS of patient-relevant outcome domains for chronic pain in IMPT clinical trials. An international, multiprofessional panel (patient representatives [n = 5], physicians specialized in pain medicine [n = 5], physiotherapists [n = 5], clinical psychologists [n = 5], and methodological researchers [n = 5]) was recruited for a 3-stage consensus study, which consisted of a mixed-method approach comprising an exploratory systematic review, a preparing online survey to identify important outcome domains, a face-to-face consensus meeting to agree on COS domains, and a second online survey (Delphi) establishing agreement on definitions for the domains included. The panel agreed on the following 8 domains to be included into the COS for IMPT: pain intensity, pain frequency, physical activity, emotional wellbeing, satisfaction with social roles and activities, productivity (paid and unpaid, at home and at work, inclusive presentism and absenteeism), health-related quality of life, and patient's perception of treatment goal achievement. The complexity of chronic pain in a biopsychosocial context is reflected in the current recommendation and includes physical, mental, and social outcomes. In a subsequent step, measurement instruments will be identified via systematic reviews.
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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 | 0.365 | 0.347 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".