Ultrasound-guided, percutaneous cryoneurolysis of intercostal nerves in high-risk, traumatic rib fracture patients
Bibliographic record
Abstract
PURPOSE: Traumatic rib fractures in high-risk patients present significant challenges in pain management, with inadequate analgesia leading to pulmonary complications, prolonged hospitalization, and increased morbidity. Conventional pain management strategies, including opioid-based regimens and catheter-based regional anesthesia, have undesirable side effects and limited duration analgesia. Cryoneurolysis overcomes many of these limitations and provides more sustained analgesia that better aligns with the expected prolonged pain trajectory of traumatic rib fractures. METHODS: This brief technical report and case series details a description of our ultrasound technique we employ for percutaneous cryoneurolysis of intercostal nerves to achieve potent analgesia for traumatic rib fractures. RESULTS: We describe five cases of severe, high-risk traumatic rib fractures, as defined by a Rib Fracture Score >6 and STUMBL Score ≥26, who received ultrasound-guided, percutaneous cryoneurolysis of intercostal nerves for analgesia. Following cryoneurolysis, all patients showed significant clinical improvements, including better pain scores, reduced opioid consumption, rapid weaning from supplemental oxygen, and accelerated rehabilitation toward hospital discharge. CONCLUSIONS: Ultrasound-guided, percutaneous cryoneurolysis represents a promising, minimally invasive technique for managing pain associated with traumatic rib fractures in high-risk patients. The procedure offers sustained analgesia, improved respiratory function, and reduced systemic analgesic requirements while maintaining a favorable risk-benefit profile.
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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 | 0.001 |
| 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".