MétaCan
Menu
Back to cohort
Record W4413776428 · doi:10.1136/rapm-2025-106915

Image-guided alcohol neurolysis for treatment of chronic hip pain secondary to avascular necrosis

2025· article· en· W4413776428 on OpenAlexaff
Abeer Alomari, Napatpaphan Kanjanapanang, Philip Peng, Nimish Mittal

Bibliographic record

VenueRegional Anesthesia & Pain Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicBone and Joint Diseases
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsNeurolysisAvascular necrosisMedicineHip painSurgeryNecrosisInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: This case series aims to assess the analgesic effectiveness and safety of percutaneous chemical denervation of the articular branches on anterior hip capsule in patients with refractory hip pain secondary to avascular necrosis (AVN). METHODS: Nine patients with refractory chronic hip pain secondary to AVN underwent image-guided chemical neurolysis with 100% ethanol. Average pain scores were recorded at baseline, 1, 3 and 6 months after chemical neurolysis. RESULTS: The average baseline pain score was 6.7±1.2 on the Numerical Rating Scale (NRS). Two out of the nine patients did not respond to chemical neurolysis. For the seven patients who responded, the average NRS decreased to (2.9±1.2) at 1 month and (3.0±1.4) at 3 months. Five out of these seven patients maintained 50% or greater pain relief at both 1 and 3 months. For the five patients who completed the 6-month follow-up, the average NRS pain score was (3.0±1.7), with three of these patients maintaining 50% or greater pain relief. No one reported side effects or complications during the follow-up period. CONCLUSIONS: In conclusion, alcohol neurolysis of the hip joint can be offered as an effective and safe modality for pain control in patients with AVN who failed conservative management. This small case series serves as a pilot for future large cohort studies.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.153
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
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 head0.030
GPT teacher head0.302
Teacher spread0.272 · 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
GenreCommentary

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

Citations2
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueRegional Anesthesia & Pain MedicineSame topicBone and Joint DiseasesFrench-language works237,207