Questions Regarding “Pulsed Radiofrequency for Chronic Intractable Lumbosacral Radicular Pain: A Six-Month Cohort Study”
Bibliographic record
Abstract
Dear Editor, I read the paper on the results of pulsed radiofrequency (PRF) for chronic intractable pain by Van Boxem et al. [ 1 ] with great interest. I congratulate the authors on this excellent prospective observational study with relevant inclusion and exclusion criteria. However, I have some questions and comments regarding the methodology and results of the study. First, the flexibility of conventional medical management (CMM) makes it difficult to interpret outcomes following PRF in participants in the study. I wonder whether allowing participants only specific analgesics (e.g., tramadol) in the post-procedure phase, measuring their mean daily doses, and limiting the use and doses of other medications for neuropathic pain (e.g., allowing only gabapentin and amitriptyline in specific dose ranges) would have helped readers to discern the impact of PRF on pain and quality of life. Escalation of doses of these medications may have accounted for post-intervention improvements and confounded the results of PRF. Second, were any steroids administered in the neural foramen following the PRF (i.e., prior to removal of PRF cannula)? This is often done by interventional pain physicians to “enhance” analgesic impact. Third, the authors used DN4 for linear follow-up and reported mean scores at various post-procedure follow-up time points. I have some concerns regarding the use of DN4 in this fashion. The DN4 is a screening tool that is often used as an inclusion criterion to increase the probability of enrolling participants with neuropathic pain, but it has not been developed or validated for the quantification of neuropathic pain or assessment of the effects of treatment. It has been suggested that the Neuropathic Pain Symptom Inventory (NPSI), a self-questionnaire specifically designed to evaluate the different symptoms of neuropathic pain, may be a more appropriate tool for assessing the effects of interventions on neuropathic pain over time [ 2 ]. Fourth, the number of patients whose data were analyzed is unclear. The flow diagram in Figure 1 indicates that 65 participants were treated with PRF, but data from 13 subjects were not included. This should have yielded data from 52 (and not 42, the number indicated in the box in the bottom left in Figure 1) participants. Fifth, though the authors indicate that the Leeds Assessment of Neuropathic Symptoms and Signs (LANSS) tool was used at each of the follow-up visits, no data from this tool were presented, except for the baseline values. Finally, even though repeat PRF was performed on only five participants, the indications and methodology for repeat PRF procedures are unclear. The authors state that a single repeat PRF procedure was performed at the same or an adjacent level according to clinical symptoms “if patients at the 6 week evaluation reported clinically insufficient pain relief, defined as less than 50% pain relief…or less than a 2 point reduction on the NRS” or “if the patient considered the effect of the first intervention was not sufficient.” The latter criterion (“not sufficient”) sounds quite subjective. Could the authors kindly elaborate on this aspect? This will help pain interventionists and their patients to decide whether repeat PRF procedures are indicated as part of clinical care or clinical trials.
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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 | 0.015 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.017 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".