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Record W3113100883 · doi:10.1111/papr.12978

Use of Pulsed Radiofrequency for the Treatment of Discogenic Back Pain: A Narrative Review

2020· review· en· W3113100883 on OpenAlexaff
Seoyon Yang, Mathieu Boudier‐Revéret, Min Cheol Chang

Bibliographic record

VenuePain Practice · 2020
Typereview
Languageen
FieldMedicine
TopicPain Management and Treatment
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsPulsed radiofrequencyMedicineLow back painDiscographyNarrative reviewMassagePhysical therapySurgeryPain reliefIntensive care medicineAlternative medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Low-back pain (LBP) is one of the most frequently reported symptoms of patients who visit pain clinics, and a significant proportion of them have discogenic pain. Pulsed radiofrequency (PRF) stimulation is an effective treatment for various types of pain. PURPOSE: We reviewed articles which investigated the effectiveness of intradiscal PRF for controlling discogenic LBP. METHODS AND MATERIALS: We searched PubMed for papers published prior to August 7, 2020, in which intradiscal PRF was used for treating discogenic LBP. The key search phrase was (intradiscal PRF) for identifying potentially relevant articles. We included articles in which intradiscal PRF was used for controlling LBP. Review articles were excluded. RESULTS: Nine publications were included in this review. Except for one study, all other studies showed positive therapeutic outcomes after treating discogenic LBP using intradiscal PRF. However, the quality of these studies was not high. CONCLUSIONS: This review showed that intradiscal PRF appears to be a helpful treatment method for patients with discogenic LBP. Our review provides insights into the degree of evidence of the therapeutic effects of intradiscal PRF for alleviating discogenic LBP. For confirmation of the effectiveness of intradiscal PRF on discogenic LBP, more high-quality studies are necessary.

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)0.003
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.931
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
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 head0.159
GPT teacher head0.409
Teacher spread0.249 · 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.

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

Citations14
Published2020
Admission routes1
Has abstractyes

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