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Record W2432105674 · doi:10.1016/j.csbj.2016.06.003

Deep brain stimulation versus motor cortex stimulation for neuropathic pain: A minireview of the literature and proposal for future research

2016· review· en· W2432105674 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueComputational and Structural Biotechnology Journal · 2016
Typereview
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsUniversity of ManitobaUniversity of British Columbia
Fundersnot available
KeywordsNeuromodulationNeuropathic painDeep brain stimulationMedicineMotor cortexNeurosciencePhysical medicine and rehabilitationBrain stimulationNeurosurgeryStimulationPsychologyAnesthesiaPsychiatryDiseaseParkinson's diseaseInternal medicine

Abstract

fetched live from OpenAlex

The treatment of neuropathic pain remains a public health concern. A growing cohort of patients is plagued by medically refractory, unrelenting severe neuropathic pain that ruins their quality of life and productivity. For this group, neurosurgery can offer two different kinds of neuromodulation that may help: deep brain simulation (DBS) and motor cortex stimulation (MCS). Unfortunately, there is no consensus on how to perform these procedures, which stimulation parameters to select, how to measure success, and which patients may benefit. This brief review highlights the literature supporting each technique and attempts to provide some comparisons and contrasts between DBS and MCS for the treatment of neuropathic pain. Finally, we highlight the current unanswered questions in the field and suggest future research strategies that may advance the care of our patients with neuropathic pain.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.048
GPT teacher head0.378
Teacher spread0.330 · 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