Signs and symptoms of pediatric complex regional pain syndrome - type 1: A retrospective cohort study
Bibliographic record
Abstract
Background: Complex regional pain syndrome (CRPS) presents with an array of symptoms that can vary from child to child, making it difficult to diagnose and differentiate from other pain conditions such as chronic musculoskeletal (MSK) pain. Thirteen symptoms and signs are outlined in the Budapest criteria for CRPS (developed and validated for adults) but have not been well described in pediatrics. Aims: The aim of this study was to describe the signs and symptoms of pediatric CRPS type 1 (CRPS 1) and determine whether a cluster of symptoms can differentiate CRPS 1 from chronic MSK pain. Methods: A retrospective cohort study of pediatric patients with CRPS 1 and MSK pain in a pediatric pain program was conducted. Descriptive statistics were used to report demographics and pain characteristics. The chi-square test was used to evaluate differences in signs and symptoms between patients with CRPS and MSK pain. A logistic regression model was used to evaluate whether a cluster of symptoms could predict a diagnosis of CRPS 1. Results: The sample included 187 patients (99 with CRPS 1 and 88 with MSK pain); 81% were female with a mean age 14.1 years. The most prevalent CRPS symptoms were hyperalgesia (54%) and allodynia (52%). A cluster of symptoms (hyperalgesia, color changes, and range of motion) predicted the probability of a diagnosis of CRPS 1. Conclusions: A cluster of symptoms may be critical in differentiating pediatric CRPS 1 and MSK pain. Future research is needed to determine if this model is valid in external populations and to explore whether a similar model can differentiate CRPS 1 from other pain conditions (e.g., neuropathic pain).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| 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) | 0.000 | 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".