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Spinal cord stimulation for chronic pain

2004· review· en· W1836249290 on OpenAlexaff
Angela Mailis, Juan Alberto Sandoval, Rod S Taylor

Post-publication record

NatureRetraction
ReasonWithdrawn as Out of Date;
Date11/22/2013 0:00
Flagged by OpenAlex?Yes

Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.

Bibliographic record

VenueCochrane Database of Systematic Reviews · 2004
Typereview
Languageen
Field
Topic
Canadian institutionsInstitute for Work & HealthToronto Western HospitalEastern Ontario Training Board
Fundersnot available
KeywordsJadad scaleMedicineRandomized controlled trialChronic painClinical trialPhysical therapyTranscutaneous electrical nerve stimulationSpasticityAnesthesiaPhysical medicine and rehabilitationSurgeryInternal medicineCochrane Library

Abstract

fetched live from OpenAlex

BACKGROUND: Spinal cord stimulation (SCS) is a form of therapy used to treat certain types of chronic pain. It involves an electrical generator that delivers pulses to a targeted spinal cord area. The leads can be implanted by laminectomy or percutaneously and the source of power is supplied by an implanted battery or by an external radio-frequency transmitter. The exact mechanism of action of SCS is poorly understood. OBJECTIVES: To assess the efficacy and effectiveness of spinal cord stimulation in relieving certain kinds of pain, as well as the complications and adverse effects of this procedure. SEARCH STRATEGY: We searched MEDLINE and EMBASE to September 2003; the Cochrane Central Register of Controlled Trials (CENTRAL) (Issue 3, 2003); textbooks and reference lists in retrieved articles. We also contacted experts in the field of pain and the main manufacturer of the stimulators. SELECTION CRITERIA: We included trials with a control group, either randomized controlled trials (RCTs) or non-randomized controlled clinical trials (CCTs), that assessed spinal cord stimulation for chronic pain. DATA COLLECTION AND ANALYSIS: Two independent reviewers selected the studies, assessed study quality and extracted the data. One of the assessors of methodological quality was blinded to authors, dates and journals. The data were analysed using qualitative methods (best evidence synthesis). MAIN RESULTS: Two RCTs (81 patients in total) met our inclusion criteria. One was judged as being of high quality (score of 3 on Jadad scale) and the other of low quality (score of 1 on Jadad scale). One trial included patients with Complex Regional Pain Syndrome Type I (reflex sympathetic dystrophy) and the other patients with Failed Back Surgery Syndrome. The follow-up periods varied from 6 to 12 months. Both studies reported that SCS was effective, however, meta-analysis was not undertaken because of the small number of patients and the heterogeneity of the study population. REVIEWERS' CONCLUSIONS: Although there is limited evidence in favour of SCS for Failed Back Surgery Syndrome and Complex Regional Pain Syndrome Type I, more trials are needed to confirm whether SCS is an effective treatment for certain types of chronic pain. In addition, there needs to be a debate about trial designs that will provide the best evidence for assessing this type of intervention.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.024
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0240.002

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.

Opus teacher head0.203
GPT teacher head0.444
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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

Citations188
Published2004
Admission routes1
Has abstractyes

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