Complex regional pain syndrome: evidence for warm and cold subtypes in a large prospective clinical sample
Bibliographic record
Abstract
Limited research suggests that there may be Warm complex regional pain syndrome (CRPS) and Cold CRPS subtypes, with inflammatory mechanisms contributing most strongly to the former. This study for the first time used an unbiased statistical pattern recognition technique to evaluate whether distinct Warm vs Cold CRPS subtypes can be discerned in the clinical population. An international, multisite study was conducted using standardized procedures to evaluate signs and symptoms in 152 patients with clinical CRPS at baseline, with 3-month follow-up evaluations in 112 of these patients. Two-step cluster analysis using automated cluster selection identified a 2-cluster solution as optimal. Results revealed a Warm CRPS patient cluster characterized by a warm, red, edematous, and sweaty extremity and a Cold CRPS patient cluster characterized by a cold, blue, and less edematous extremity. Median pain duration was significantly (P < 0.001) shorter in the Warm CRPS (4.7 months) than in the Cold CRPS subtype (20 months), with pain intensity comparable. A derived total inflammatory score was significantly (P < 0.001) elevated in the Warm CRPS group (compared with Cold CRPS) at baseline but diminished significantly (P < 0.001) over the follow-up period, whereas this score did not diminish in the Cold CRPS group (time × subtype interaction: P < 0.001). Results support the existence of a Warm CRPS subtype common in patients with acute (<6 months) CRPS and a relatively distinct Cold CRPS subtype most common in chronic CRPS. The pattern of clinical features suggests that inflammatory mechanisms contribute most prominently to the Warm CRPS subtype but that these mechanisms diminish substantially during the first year postinjury.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".