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Record W2590564190 · doi:10.1093/neuros/nyw047

The Advancing Role of Neuromodulation for the Management of Chronic Treatment-Refractory Pain

2017· review· en· W2590564190 on OpenAlexaff
Mohammed F. Shamji, Cecile De Vos, Ashwini Sharan

Bibliographic record

VenueNeurosurgery · 2017
Typereview
Languageen
FieldMedicine
TopicPain Management and Treatment
Canadian institutionsToronto Western HospitalKrembil FoundationUniversity of Toronto
Fundersnot available
KeywordsMedicineNeuromodulationNeuropathic painChronic painIntensive care medicineDeep brain stimulationPhysical medicine and rehabilitationPhysical therapyDiseaseAnesthesiaStimulationParkinson's diseaseInternal medicine

Abstract

fetched live from OpenAlex

Neuropathic pain is a common cause of disability and health care utilization. While judicious pharmacotherapy and management of comorbid psychological distress can provide for improved quality of life, some patients with treatment-refractory disease require more invasive therapies. Spinal cord stimulation can provide for improvement in pain and decrease in medication utilization, with level 1 evidence supporting its use across various pain etiologies including persistent postoperative neuropathic pain, complex regional pain syndrome, chronic inoperable limb ischemia, treatment refractory angina, and painful diabetic neuropathy. These procedures can be done with acceptably low morbidity and provide a cost-effective solution for those patients in whom medical therapies have failed. Technological innovation in lead design, implantable pulse generator capability, and stimulation algorithms and parameters may further enhance the success of this therapy. Neuromodulation of distal targets such as dorsal root ganglion may permit greater anatomic specificity of the therapy, whereas subthreshold stimulation with high-frequency or burst energy delivery may eliminate noxious and off-target paresthesiae. Such new technologies should be subject to rigorous evaluation as their mechanisms of action and long-term outcomes remain hitherto undefined.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.069
GPT teacher head0.350
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations40
Published2017
Admission routes1
Has abstractyes

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