Genicular nerve radiofrequency ablation, phenol neurolysis or conservative medical management in patients with knee osteoarthritis: protocol for the RADIOPHENOL randomised controlled multicentre trial with three parallel groups
Bibliographic record
Abstract
INTRODUCTION: Guidelines for symptomatic knee osteoarthritis (OA) dictate the initiation of conservative treatment (physical therapy, analgesics and intra-articular injections with corticosteroids) as a first line defence. When conservative treatment fails, the golden standard is invasive joint replacement surgery, but for a substantial group of patients who do not respond to the current conservative treatment, this is not (yet) indicated. The RADIOPHENOL study investigates if denervation of knee sensory (genicular) nerves can serve the gap between conservative and invasive treatment for younger patients and for patients who cannot undergo joint replacement surgery due to comorbid health conditions. METHODS AND ANALYSIS: The RADIOPHENOL study is a multicentre unblinded randomised controlled trial with three parallel arms (1:1:1). In total, 192 patients with knee OA Kellgren-Lawrence grades 2-4 but not eligible for joint replacement according to the orthopaedic surgeon due to young age, old age and/or comorbidity or technical reasons are eligible and will be randomised to three groups of 64 patients. Group A: traditional radiofrequency ablation, group B: chemical neurolysis with phenol, group C: conservative medical management. Primary outcome is the Oxford Knee Score at 6 months. Secondary outcomes include Western Ontario and McMaster Universities Osteoarthritis Index, knee pain by numeric rating scale, physical functionality, health-related quality of life, mental health, change in medication use, predictive value of a diagnostic block, procedure time, patient discomfort score during the intervention and adverse events. ETHICS AND DISSEMINATION: The protocol (V.2.0, 15 May 2023), was approved by the Ethics Committee of Amsterdam UMC (NL83410.018.22 - METC2022.0890) on 31 July 2023. We aim to publish our results in international peer-reviewed journals. TRIAL REGISTRATION DETAILS: ClinicalTrials.gov NCT06094660, including the WHO Trial Registration data set items. Registered on 20 October 2023, first patient enrolled on 27 November 2023.
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 | 0.026 | 0.026 |
| Meta-epidemiology (narrow) | 0.008 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.008 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.052 | 0.010 |
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".