Cervical epidural analgesia via a thoracic approach using nerve‐stimulation guidance in adult patients undergoing total shoulder replacement surgery
Bibliographic record
Abstract
BACKGROUND: Continuous cervical epidural anesthesia can provide excellent peri- and post-operative analgesia, although several factors prevent its widespread use. Advancing catheters from thoracic levels to the cervical region may circumvent these barriers, provided they are accurately positioned. We hypothesize that guiding catheters from thoracic to cervical regions using low-current epidural stimulation will have a high success rate and enable excellent analgesia in adults undergoing total shoulder arthroplasty. METHODS: After Institutional Review Board approval, adult patients were studied consecutively. A 17-G Tuohy needle was inserted into the thoracic epidural space using a right paramedian approach with loss of resistance. A 20-G styletted epidural catheter, with an attached nerve stimulator, was primed with saline and a 1-10 mA current was applied as it advanced in a cephalad direction towards the cervical spine. Muscle twitch responses were observed and post-operative X-ray confirmed final placement. After a test dose, an infusion (2-8 ml/h) of ropivacaine 2 mg/ml and morphine 0.05 mg/ml (or equivalent) was initiated. Verbal analog pain scale scores were collected over 72 h. RESULTS: Cervical epidural anesthesia was performed on 10 patients. Average current required to elicit a motor response was 4.8 +/- 2.0mA. Post-operative X-ray of catheter positions confirmed all catheter tips reached the desired region (C4-7). The technical success rate for catheter placement was 100% and excellent pain control was achieved. Catheters were positioned two to the left, four to the right and four to the midline. CONCLUSION: This epidural technique provided highly effective post-operative analgesia in a patient group that traditionally experiences severe post-operative pain and can benefit from early mobilization.
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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 | 0.001 | 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".