Screening and diagnostic tools for complex regional pain syndrome: a systematic review
Bibliographic record
Abstract
ABSTRACT: Complex regional pain syndrome (CRPS) is a severely painful condition that presents with a constellation of symptoms. The understanding of the pathophysiology of CRPS has evolved over time, as have the diagnostic criteria. Our primary objective was to identify screening and diagnostic tools for CRPS and summarize their feasibility, measurement properties, and study quality. A secondary objective was to identify screening and diagnostic tools used for CRPS in pediatric populations (0-21 years of age). A systematic review of English articles in electronic databases (PsycINFO, MEDLINE, Embase, CINAHL, CENTRAL, and Web of Science) was conducted with the aid of a librarian in November 2018 and updated in July 2020. Studies were included if the tool was a screening or diagnostic tool, the tool included self-report or physical examination, and the primary objective of the study was to evaluate the measurement properties or feasibility of use. For each study, data were extracted for quality indicators using the QUADAS-2 tool. No screening tools were identified. Four diagnostic tools were identified: the Veldman criteria, International Association for the Study of Pain criteria, Budapest Criteria, and Budapest Research Criteria. There are no diagnostic tools validated for use in pediatric CRPS. Because there are no extant screening tools for CRPS, all people with suspected disease should undergo rapid diagnostic assessment by a clinician. For adults, the Budapest Criteria are the preferred diagnostic tool. Future research is recommended to develop a diagnostic tool for pediatric populations and screening tools for both pediatric and adults.
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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 | 0.017 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".