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Record W4396616308 · doi:10.20471/acc.2023.62.s4.5

Therapeutic Genicular Nerve Block for Chronic Pain Management in Patients with Knee Osteoarthritis

2023· article· en· W4396616308 on OpenAlexaboutno aff
Vlasta Orlić Karbić

Bibliographic record

VenueActa Clinica Croatica · 2023
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsnot available
Fundersnot available
KeywordsOsteoarthritisMedicinePain managementKnee painBlock (permutation group theory)Nerve blockPhysical therapyPhysical medicine and rehabilitationSurgeryAlternative medicinePathology

Abstract

fetched live from OpenAlex

Background: Therapeutic genicular nerve block (TGNB) is both an effective and safe treatment procedure for pain related to chronic osteoarthritis of the knee (OA). It is most common amongst the elderly. It is characterized by joint stiffness, pain and disability. Aim: The aim of this study was to examine the analgesic and functional impact of ultrasound-guided TGNB in patients with chronic knee OA and to evaluate the efficacy of local anesthetics and corticosteroids. Patients and methods: The study included 20 patients. Pain was assessed according to the numerical pain scale (NRS), and improvement of the functional capacity was assessed according to the WOMAC Index (Western Ontario and McMaster Universities Osteoarthritis Index). SLGN, SMGN and IMGN were identified, and 3 mL of a mixture of local anesthetic (ropivacaine 0.75%) and corticosteroid (triamcinolone 40 mg) were applied to each nerve. Results: The average NRS value before TGNB was 5.1, and the WOMAC score was 58.55. After the TGNB was performed, NRS was 2.4 (47% decrease in pain intensity) and WOMAC was 30.1 (51% decrease in the intensity of ailment). Conclusion: TGNB is effective and not harmful in treating pain and enhances the functional capacity in patients with knee OA. The clinical benefits of corticosteroid administration suggest that it may be an appropriate adjuvant in TGNB for knee OA.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.377
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.018
GPT teacher head0.278
Teacher spread0.259 · 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 designObservational
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

Citations1
Published2023
Admission routes1
Has abstractyes

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