Posterior hip pericapsular neurolysis (PHPN) for inoperable hip fracture: an adjunct to anterior hip pericapsular neurolysis
Bibliographic record
Abstract
BACKGROUND: The development of anterior hip neurolysis has made an appreciable impact on the management of patients with inoperable hip fracture. Nonetheless, suboptimal analgesic benefit was still observed in some patients. We therefore developed a novel posterior hip pericapsular neurolysis (PHPN) to complement anterior hip neurolysis in inoperable hip fracture. METHODS: In this retrospective review, we analyzed patients who responded suboptimally (defined as composite pain score moderate or higher on hip flexion 80°) to the anterior hip neurolysis from July 2019 to March 2021. The patients received additional PHPN if the composite pain score was suboptimal. The percentage of patients with dynamic composite pain scores on hip flexion to 80° and on gentle hip external and internal rotation on post-intervention day 5 was then evaluated. RESULTS: Among the 70 patients who were reviewed in the study period, 20 patients who partially responded to anterior hip neurolysis received an addition of diagnostic posterior hip pericapsular block. Eighteen patients were the positive responders and PHPN was then performed. Among the PHPN recipients, a high percentage with satisfactory dynamic pain control was observed with 78% on hip flexion 80° and 83% on hip external/internal rotation on day 5 following the intervention. No procedural adverse event was noted. CONCLUSIONS: While most patients responded satisfactorily to anterior hip neurolysis, we concluded PHPN could be an effective adjunct to manage suboptimal pain control after anterior hip neurolysis in inoperable hip fracture. TRIAL REGISTRATION NUMBER: NTWC/REC/21061.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".