Chemical Hip Denervation for Inoperable Hip Fracture
Bibliographic record
Abstract
BACKGROUND: Hip fracture is a challenging geriatric problem for the health care professionals, especially in patients with multiple comorbidities. In patients with inoperable hip fracture secondary to severe comorbid conditions, the pain can lead to significant challenges in nursing care. With the current understanding of the innervation of hip joint, we are now able to perform selective chemical denervation of the articular branches of femoral and obturator nerves to manage the pain associated with inoperable hip fracture. METHODS: In this retrospective case series, we analyzed 20 consecutive patients with inoperable hip fracture who received chemical denervation and examined the effect of the denervation on pain and functional outcomes, including the maximally tolerable hip flexion and the ability to sit during their hospital stay. We also assessed the likelihood of being ambulatory as a long-term outcome. RESULTS: The movement-related pain was significantly reduced at 10 minutes postprocedure, on postintervention days 1 and 5 (P values of <.001), and the degree of maximally tolerable hip flexion was doubled at the same time points (P values of <.001, .003, and .002, respectively). Fifty percent of the patients managed to sit within the first 5 days after procedure, and 3 of them managed to walk with aid 4 months after hip denervation. No procedural adverse event was noted. CONCLUSIONS: We concluded that this chemical hip denervation could be a safe and effective measure to handle the pain-related and rehabilitation-related challenges as a result of inoperable hip fracture.
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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".