Characteristics of Complex Regional Pain Syndrome in Patients Referred to a Tertiary Pain Clinic by Community Physicians, Assessed by the Budapest Clinical Diagnostic Criteria
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to describe the characteristics of patients referred with complex regional pain syndrome (CRPS) diagnosis to a tertiary care pain center. METHOD: Descriptive chart review study of all patients referred by family physicians or community specialists as having CRPS (2006-2010). Data extraction included demographics, pain ratings, and diagnosis utilizing the Budapest CRPS criteria. RESULTS: The study population consisted of 54 subjects (male [M] =7, female [F] =47). Only 27.7% were classified as CRPS by the clinical expert. Four additional subjects carrying other diagnoses but found to have CRPS were added to the analysis. The non-CRPS group consisted of 39 subjects (M=8, F=31) and the CRPS group of 19 (M=2, F=17). CRPS patients were statistically significantly more likely to 1) have suffered a fracture; 2) report symptoms in each of the four symptom categories, as well as signs in three or four categories collectively; and 3) have allodynia/hyperalgesia alone or in combination (85/90%) as compared with the non-CRPS group (23/25%, respectively). The non-CRPS group was much more likely to report no symptoms or signs at all in the different symptom and sign categories. Of the 39 non-CRPS patients, 74% had other diagnosable entities (1/3 suffering from specific neuropathic pain conditions, e.g., radiculopathy, diabetic neuropathy, etc. and 2/3 from discreet musculoskeletal entities), while 18% were diagnosed with psychogenic pain disorders including conversion reaction associated with immobility or paralysis. DISCUSSION: Besides fulfilling the Budapest CRPS diagnostic criteria, the most important other factor for diagnosing CRPS is the exclusion of a neuropathic, musculoskeletal, or non-biomedical condition accounting for the presentation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".