Neurostimulation for chronic noncancer pain: an evaluation of the clinical evidence and recommendations for future trial designs
Bibliographic record
Abstract
OBJECT: Neurostimulation to treat chronic pain includes approved and investigational therapies directed at the spinal cord, thalamus, periaqueductal or periventricular gray matter, motor cortex, and peripheral nerves. Persistent pain after surgery and work-related or neural injuries are common indications for such treatments. In light of the risks, efforts, costs, and expectations associated with neurostimulation therapies, a careful reexamination of the methods used to gather evidence for this treatment's long-term efficacy is in order. METHODS: The authors combed English-language publications to determine the nature of the evidence supporting the efficacy of neurostimulation therapies for chronic noncancer pain. To formulate recommendations for the design of future studies, the results of their analysis were compared with established guidelines for the evaluation of medical evidence. Evidence supporting the efficacy of neurostimulation has been collected predominantly from retrospective series or from prospective studies whose design or methods of analysis make them subject to limited interpretation. To date, there has been no successful clinical study focused on establishing the efficacy of neurostimulation for pain and incorporating sufficient numbers of participants, matched control groups, sham stimulation, randomization, prospectively defined end points, and methods for controlling experimental bias. Currently available data provide little support for the common practices of psychological or pharmacological screening or trial stimulation to predict and/or improve long-term results. CONCLUSIONS: These findings do not diminish the value of previous investigations or positive patient experiences and do not mean that the treatments are ineffective; rather, they reveal that new data are required to answer the questions raised in and by previous study data. Future analyses of emerging neurostimulation modalities for pain should, whenever feasible, require unambiguous diagnoses as an entry criterion and should involve the use of randomization, parallel control groups that receive sham stimulation, and blinding of patients, investigators, and device programmers. Given the chronicity of patient symptoms and stimulation therapies, efficacy should be studied for 1 year or longer after device implantation. Meticulous study methods are especially important to evaluate new therapies like motor cortex and occipital nerve stimulation.
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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 | 0.570 | 0.706 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.016 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.020 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".