Complex Regional Pain Syndrome: Diagnostic and Treatment Conundrum
Bibliographic record
Abstract
A 54-year-old man presented with a 12-week history of right lower extremity radicular pain with symptoms and signs of complex regional pain syndrome (CRPS). He reported a recent medical history of transient ischemic attack followed by cerebrovascular accident for which he underwent carotid endarterectomy. Following carotid endarterectomy, the patient was left with minimal residual speech and memory deficits, but concomitant presentation of right lower extremity radicular pain in the postoperative period was the reason for seeking pain relief. Right lumbar radiculopathy was suspected at the time of presentation to the pain clinic, and magnetic resonance imaging revealed bilateral moderate to severe lumbar foraminal stenosis, worse on the right side. Parasagittal lumbar epidural steroid injection, and aggressive multimodal pain management strategy was started resulting in significant improvement in pain scores, functionality and stress levels, albeit for a short period. Patient continued to obtain incomplete resolution of symptoms with conventional treatment. He underwent spinal cord stimulation at an early stage but only derived moderate benefit. The presented case is unusual because of overlapping etiologies that influenced our treatment plans. In this article, we will discuss the evidence for each of the questions raised by practitioners while treating aforementioned patient with CRPS. J Med Cases. 2016;7(1):33-42 doi: http://dx.doi.org/10.14740/jmc2378w
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| 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".