ESRA19-0322 Femoral nerve pulsed-radiofrequency (PRF) therapy in the treatment of complex regional pain syndrome (CRPS) following total knee replacement
Bibliographic record
Abstract
Background and aims Pulsed radiofrequency (PRF) is being increasingly used as a treatment option for chronic neuropathic pain syndromes. The authors report regarding, to their knowledge, the novel use of PRF of the femoral nerve in the pain management of a 66-year-old female, diagnosed with CRPS in accordance with the Budapest consensus criteria following unilateral total knee replacement surgery. Methods Three consecutive sessions, with 2-month intervals, of ultrasound guided femoral nerve PRF therapy were administered to the affected CRPS limb applying 6 min, 42 °C, 45V, 5Hz, 5 Ms. Pre-treatment Brief Pain Inventory (BPI) and McGill-Pain-Questionnaire Short-Form (MPQ) were recorded and subsequently repeated at the completion of the proposed treatment, supplemented with the Patient’s Global Impression of Change Scale. Results The subject reported objective improvement in symptoms and associated function following the sessions of PRF when comparison of the baseline BPS/MPQ-SF were analysed. Clinical apparent improvements in swelling, vasomotor instability, sudomotor abnormality, and motor function of the affected lower limb were noted with associated reduction in required oral analgesic medications. Conclusions Pulsed radiofrequency of femoral nerve has the potential to be a useful treatment modality in the pain management of CRPS affecting this anatomical region and further exploration of its utility is required.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".