Recommendations for a first Core Outcome Measurement set for complex regional PAin syndrome Clinical sTudies (COMPACT)
Bibliographic record
Abstract
Complex regional pain syndrome (CRPS) is a persistent pain condition that remains incompletely understood and challenging to treat. Historically, a wide range of different outcome measures have been used to capture the multidimensional nature of CRPS. This has been a significant limiting factor in the advancement of our understanding of the mechanisms and management of CRPS. In 2013, an international consortium of patients, clinicians, researchers, and industry representatives was established, to develop and agree on a minimum core set of standardised outcome measures for use in future CRPS clinical research, including but not limited to clinical trials within adult populations. The development of a core measurement set was informed through workshops and supplementary work, using an iterative consensus process. "What is the clinical presentation and course of CRPS, and what factors influence it?" was agreed as the most pertinent research question that our standardised set of patient-reported outcome measures should be selected to answer. The domains encompassing the key concepts necessary to answer the research question were agreed as follows: pain, disease severity, participation and physical function, emotional and psychological function, self-efficacy, catastrophizing, and patient's global impression of change. The final core measurement set included the optimum generic or condition-specific patient-reported questionnaire outcome measures, which captured the essence of each domain, and 1 clinician-reported outcome measure to capture the degree of severity of CRPS. The next step is to test the feasibility and acceptability of collecting outcome measure data using the core measurement set in the CRPS population internationally.
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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 | 0.564 | 0.767 |
| Meta-epidemiology (narrow) | 0.004 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.031 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.015 | 0.016 |
| Research integrity | 0.027 | 0.030 |
| Insufficient payload (model declined to judge) | 0.019 | 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; 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".