Image-guided alcohol neurolysis for treatment of chronic hip pain secondary to avascular necrosis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".