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Record W4398918748 · doi:10.7910/dvn/tlnaur

Comparison of the efficacy of genicular nerve phenol neurolysis and radiofrequency ablation for pain management in patients with knee osteoarthritis

2023· dataset· en· W4398918748 on OpenAlexaboutno aff
Gökhan Yıldız, Gevher Rabia Genç Perdecioğlu, Damla Yürük, Ezgi Can, Taylan Akkaya

Bibliographic record

VenueHarvard Dataverse · 2023
Typedataset
Languageen
FieldMedicine
TopicHealthcare and Venom Research
Canadian institutionsnot available
Fundersnot available
KeywordsNeurolysisOsteoarthritisMedicineRadiofrequency ablationKnee painPain managementSaphenous nerveSurgeryAblationPhysical therapyInternal medicinePathologyAlternative medicine

Abstract

fetched live from OpenAlex

Genicular nerve neurolysis with phenol and radiofrequency ablation (RFA) are two interventional techniques for treating chronic refractory knee osteoarthritis (KOA) pain. This study aimed to compare the efficacy and adverse effects of both techniques. Sixty-four patients responding to diagnostic blockade of the superior medial, superior lateral, and inferior medial genicular nerve under ultrasound guidance were randomly divided into two groups: Group P (2 mL phenol for each genicular nerve) and Group R (RFA 80°C for 60 seconds for each genicular nerve). The numeric rating scale (NRS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) were used to evaluate the effectiveness of the interventions.

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)0.003
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.011
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.030
GPT teacher head0.318
Teacher spread0.289 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2023
Admission routes1
Has abstractyes

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