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Record W2977482812 · doi:10.1111/nyas.14236

Opioid‐sparing effects of 10 kHz spinal cord stimulation: a review of clinical evidence

2019· review· en· W2977482812 on OpenAlexaff
Adnan Al‐Kaisy, Jean‐Pierre Van Buyten, Kasra Amirdelfan, Bradford E. Gliner, David Caraway, Jeyakumar Subbaroyan, Anand Rotte, Leonardo Kapural

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2019
Typereview
Languageen
FieldMedicine
TopicPain Management and Treatment
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsMedicineChronic painOpioidSpinal cord stimulationAnesthesiaTrunkIntractable painPain reliefPhysical therapyStimulationInternal medicine

Abstract

fetched live from OpenAlex

Chronic pain is a common condition that affects the physical, emotional, and mental well-being of patients and can significantly diminish their quality of life. Due to growing concerns about the substantial risks of long-term opioid use, both governmental agencies and professional societies have recommended prioritizing the use of nonpharmacologic treatments, when suitable, in order to reduce or eliminate the need for opioid use. The use of 10 kHz spinal cord stimulation (10 kHz SCS) is one such nonpharmacologic alternative for the treatment of chronic, intractable pain of the trunk and limbs. This review examines published clinical data regarding the efficacy of 10 kHz SCS for decreasing chronic pain in patients and its potential to reduce or eliminate opioid usage. Multiple prospective and retrospective studies in patients with intractable pain demonstrated that 10 kHz SCS treatment provided ≥50% pain relief in >70% patients after at least 1 year of treatment. Pain relief with 10 kHz SCS therapy ranged from 54% to 87% in the studies. More importantly, the mean daily dose of opioids required by patients in these studies was reduced after 10 kHz SCS treatment, and on average over 60% patients in studies either reduced or eliminated opioids at the last follow-up.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.739
Threshold uncertainty score0.631

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.477
GPT teacher head0.541
Teacher spread0.065 · 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 designSystematic review
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

Citations64
Published2019
Admission routes1
Has abstractyes

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