Early Treatment of Acute Complex Regional Pain Syndrome after Fracture or Injury with Prednisone: Why Is There a Failure to Treat? A Case Series
Bibliographic record
Abstract
Background. Complex regional pain syndrome (CRPS) after fracture is a cause of pain, dysfunction, and potentially permanent disability. The evidence for treatment with oral corticosteroids is growing and supported by several international guidelines; however, treatment is not widely offered. Objective. Rapid recognition and treatment of complex regional pain in the upper extremity after acute injury as a disease modifying and potentially curative treatment. Methods. The present study was a case series involving three patients who developed CRPS after a trauma to the neck and/or upper limb. Patients were screened by clinical examination and bone scan and met the Budapest criteria. Results. Resolution of pain, swelling, and disability in all three patients. Discussion. There is increasing support, based on the existing evidence and clinical outcomes, for the use of prednisone to treat the acute phase of CRPS and as a promising treatment to halt the progression of the phenomenon and potentially cure the condition; however, widespread use of prednisone likely remains low, potentially resulting in long-term pain, joint contracture, and disability. A large-scale randomized control trial has not been performed. Conclusion. Corticosteroids can be an effective treatment option for CRPS after fracture.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.008 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".