Assessment of physical function and participation in chronic pain clinical trials: IMMPACT/OMERACT recommendations
Bibliographic record
Abstract
Although pain reduction is commonly the primary outcome in chronic pain clinical trials, physical functioning is also important. A challenge in designing chronic pain trials to determine efficacy and effectiveness of therapies is obtaining appropriate information about the impact of an intervention on physical function. The Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) and Outcome Measures in Rheumatology (OMERACT) convened a meeting to consider assessment of physical functioning and participation in research on chronic pain. The primary purpose of this article is to synthesize evidence on the scope of physical functioning to inform work on refining physical function outcome measurement. We address issues in assessing this broad construct and provide examples of frequently used measures of relevant concepts. Investigators can assess physical functioning using patient-reported outcome (PRO), performance-based, and objective measures of activity. This article aims to provide support for the use of these measures, covering broad aspects of functioning, including work participation, social participation, and caregiver burden, which researchers should consider when designing chronic pain clinical trials. Investigators should consider the inclusion of both PROs and performance-based measures as they provide different but also important complementary information. The development and use of reliable and valid PROs and performance-based measures of physical functioning may expedite development of treatments, and standardization of these measures has the potential to facilitate comparison across studies. We provide recommendations regarding important domains to stimulate research to develop tools that are more robust, address consistency and standardization, and engage patients early in tool development.
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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 | 0.438 | 0.603 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.021 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.012 | 0.011 |
| Research integrity | 0.024 | 0.023 |
| Insufficient payload (model declined to judge) | 0.008 | 0.010 |
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".