A systematic review of psychometric evaluations of outcome assessments for complex regional pain syndrome
Bibliographic record
Abstract
Purpose: To conduct a systematic review of the quality and extent of psychometric examinations of disease-specific outcome measures for complex regional pain syndrome (CRPS). Methods: Health database searches yielded 23 papers covering 19 assessment instruments. Each article was scored for quality using a 12-item structured tool; data were also extracted for comparison of tool content. Results: Article quality ratings ranged from 25 to 88%. Six of the tools were specific to the upper extremity; 5 for the lower extremities while the remaining 8 were general. Many ‘general’ tools focused on a single construct, such as pain, skin temperature or allodynia. Most psychometric data was based on small studies (mean n = 33); only one study addressed all relevant issues of reliability, validity and responsiveness. Conclusions: Despite the variety of outcome measurement tools reported for CRPS rehabilitation, large gaps in both comprehensiveness and supporting psychometric evidence remain. Comprehensive, relevant and psychometrically sound tools for monitoring treatment outcomes are needed to address the pain and functional limitations experienced by this population.Implications for RehabilitationComplex regional pain syndrome (CRPS) is a neurological disorder with signs and symptoms that vary with activity, environment and stress.Although there is no diagnostic test for this syndrome, a need exists for tools to monitor treatment outcomes that address the pain and functional limitations experienced by this population.This review suggests that at present, there is no single comprehensive outcome measure for clinical practice and/or research which has strong supporting psychometric evidence for the evaluation of persons with CRPSAPPENDIX, a scoring sheet and scoring guidelines for critical appraisals for the evaluation of persons with CRPS.
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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 | 0.029 | 0.144 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".