VP191 Peripheral Nerve Field Stimulation For Chronic Low Back Pain
Bibliographic record
Abstract
INTRODUCTION: Despite numerous medical, pharmacological and surgical approaches for chronic low back pain (LBP), many patients continue to complain of severe disabling pain. Peripheral nerve field stimulation (PNfS), alone or combined with spinal cord stimulation, is a neuromodulation procedure that have been recently developed and implemented in our hospital. We conducted a Health Technology Assessment (HTA) to determine if PNfS may be considered as a standard of practice in the management of intractable LBP and failed back surgery syndrome (FBSS). METHODS: An interdisciplinary group of experts was involved in the project. A systematic review (SR) was performed in several databases and grey literature to identify clinical practice guidelines, SR and observational studies published through September 2016. A survey was conducted among other chronic pain centers in Canada to document PNfS use in LBP and FBSS treatment. RESULTS: Data on effectiveness and safety of PNfS in chronic LBP treatment were scarce. Short-term results (3-12 months) from small sample and low quality studies suggest that PNfS, alone or combined with spinal cord stimulation, is associated with pain intensity and opioid use reductions. Effects on functional status and quality of life remain undetermined. Most frequent adverse events reported with PNfS devices are lead migrations, discomfort or pain and surgical site infections. No other Canadian pain centers were found to use PNfS in chronic LBP or FBSS. CONCLUSIONS: PNfS is potentially a beneficial treatment option for patients with chronic low back pain or FBSS. However, the value of this innovative treatment remains unknown. Among factors to be clarified are target population (any chronic low back pain or FBSS), use of PNfS alone or combined with spinal cord stimulation, long-term effects, and comparison with conventional medical management. PNfS use in chronic LBP has to be assessed through a rigorous framework before its introduction as a standard medical practice.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".