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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".