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Record W4416278267 · doi:10.1097/aco.0000000000001592

Ethics, economics, and patient-centered outcomes of spinal cord stimulation

2025· article· en· W4416278267 on OpenAlexaff
Ryan S. D’Souza, Harsha Shanthanna

Bibliographic record

VenueCurrent Opinion in Anaesthesiology · 2025
Typearticle
Languageen
FieldMedicine
TopicPain Management and Treatment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSpinal cord stimulationSpinal cordMEDLINEClinical trialCurrent (fluid)

Abstract

fetched live from OpenAlex

PURPOSE OF THE REVIEW: With the widespread integration of spinal cord stimulation (SCS) into clinical practice, understanding its ethical, economic, and patient-centered implications has become increasingly important. This review critically examines recent evidence across these domains to illuminate challenges and opportunities for advancing transparent, ethical, patient-centered, and value-based neuromodulation practice. RECENT FINDINGS: Recent analyses reveal persistent challenges with bias, conflicts of interest, and selective outcome reporting in neuromodulation research. Studies demonstrate significant disparities in access to SCS across racial and socioeconomic groups and highlight new ethical considerations associated with artificial intelligence-enabled and informed treatment in neuromodulation. Contemporary randomized trials support clinically meaningful improvements in pain, functionality, psychological outcomes, and other patient-centered outcomes, although durability remains inconsistent because of the potential for therapy habituation or adverse events. Despite high initial upfront costs, evaluations of SCS cost-effectiveness across healthcare systems generally favor SCS over conventional medical management when assessed over multiyear periods, with differential economic benefits observed based on the type of waveform and type of national healthcare system. SUMMARY: While current evidence supports the clinical and long-term economic benefits of SCS in defined clinical contexts and indications, gaps in research transparency, equitable access to care, economic considerations, and durability of effectiveness persist.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.088
GPT teacher head0.388
Teacher spread0.300 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes1
Has abstractyes

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