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Record W4286487854 · doi:10.1093/pm/pnac108

Radiofrequency Ablation of the Infrapatellar Branch of the Saphenous Nerve for the Treatment of Chronic Anterior Inferomedial Knee Pain

2022· article· en· W4286487854 on OpenAlexaff
Margaret Beckwith, Daniel M. Cushman, Tyler Clark, Peter K. Park, Taylor Burnham, Robert Burnham, Zachary L. McCormick, Aaron Conger

Bibliographic record

VenuePain Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversity of Alberta
FundersUniversity of Utah
KeywordsMedicineRadiofrequency ablationSaphenous nervePulsed radiofrequencySurgeryOsteoarthritisAblationKnee painPathologyPain reliefInternal medicine

Abstract

fetched live from OpenAlex

INTRO: Genicular nerve radiofrequency ablation (GNRFA) is an effective treatment for chronic knee pain related to osteoarthritis. It is often utilized when conservative management has failed and patients wish to avoid arthroplasty, are poor surgical candidates due to comorbid medical conditions, or in those suffering from persistent pain after arthroplasty. The classic targets for GNRFA include the superior lateral genicular nerve, superior medial genicular nerve, and inferior medial genicular nerve but multiple anatomic studies have demonstrated additional sensory innervation to the knee. OBJECTIVE: In this research article, we propose an image-guided technique that can safely target the infrapatellar branch of the saphenous nerve which also provides sensory innervation to the anterior capsule. PROPOSAL: The proposed technique includes variations for conventional bipolar radiofrequency ablation, cooled radiofrequency ablation, dual-tined bipolar radiofrequency ablation, and monopolar radiofrequency ablation using a long axis approach. The described technique is based on updated anatomic studies and takes into account safety concerns such as thermal risk to the skin and/or pes anserine tendons and breaching of the synovial cavity. CONCLUSION: Future clinical research should be performed to confirm the safety and effectiveness of this specific approach.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.713
Threshold uncertainty score0.329

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.000
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 head0.019
GPT teacher head0.280
Teacher spread0.261 · 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 designOther design
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

Citations12
Published2022
Admission routes1
Has abstractyes

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