Anti tumor Necrosis Factor ‐ Alpha Adalimumab for Complex Regional Pain Syndrome Type 1 (CRPS‐I): A Case Series
Bibliographic record
Abstract
BACKGROUND AND AIMS: Evidence suggests tumor necrosis factor-alpha (TNF-α) mediates, at least in part, symptoms and signs in complex regional pain syndrome (CRPS). Here, we present a case series of patients with CRPS type 1, in whom the response to the anti-TNF-α adalimumab was assessed. METHODS: Ten patients with CRPS type 1 were recruited. Assessments were performed before treatment, at 1 week, and 1, 3, and 6 months following 3 biweekly subcutaneous injections (40 mg/0.8 mL) adalimumab (Humira(®) ) and included the followings: Pain intensity using a 0-10 cm visual analog scale; the Short Form of the McGill Pain Questionnaire; the Beck Depression Inventory; the SF-36 questionnaire and mechanical and thermal thresholds (Von frey hair and Thermal Sensory Analyzer, respectively). In addition to the description of individual patient responses, both intention to treat (ITT) and per-protocol (PP) analyses were performed for the entire group. RESULTS: Three subgroups of patients were identified (3 patients in each): "nonresponders", "partial responders", and "robust responders" in whom improvement in almost all parameters was noted. Both the ITT and PP analyses demonstrated only a trend toward improvement in mechanical pain thresholds following treatment (ITT χ² = 13.83, P = 0.008; PP χ² = 10.29, P = 0.036). CONCLUSION: These results suggest adalimumab, and possibly other anti-TNF-α, can be potentially useful in some (although not in all) patients with CRPS type 1. These preliminary results along with the growing body of evidence which points to the involvement of TNF-α in the pathogenesis of CRPS justify further studies in this area.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".