Efficacy and safety of supraclavicular and pectoralis nerve blocks as primary peri‐procedural analgesia for cardiac electronic device implantation: A pilot study
Bibliographic record
Abstract
BACKGROUND: Cardiac implantable electronic devices (CIEDs) are routinely implanted using intravenous drugs for sedation. However, some patients are poor candidates for intravenous sedation. OBJECTIVE: We present a case series demonstrating the safety and efficacy of a novel, ultrasound-guided nerve block technique that allows for pre-pectoral CIED implantation. The targets are the supraclavicular nerve (SCN) and pectoral nerve (PECS1). METHODS: We enrolled 20 patients who were planned for new CIED implantation. Following US-localization of the SCN and PECS1, local anesthetic (LA) was instilled at least 30-60 min pre-procedure. Successful nerve block was determined if < 5 mL of intraprocedural LA was used, along with lack of sensation with skin and deep tissue pinprick. Optional sedation was offered to patients' pre-procedure if discomfort was reported. RESULTS: Seventeen patients (85%) had a successful periprocedural nerve block, with only three patients exceeding 5 mL of LA. SCN and PECS1 success occurred in 19 (95%) and 18 (90%) patients, respectively. The overall success of nerve block by fulfilling all the criteria was demonstrated in 17 out of 20 patients (85%). Patients who reported no pain (VAS score = 0) were distributed as follows: 13 patients (65%) in the immediate post-procedure interval, 18 patients (90%) at the 1 h post-implant interval, and 14 patients (70%) at the 24 h post- implant interval. The median cumulative VAS score was 0 (IQR = 0 - 1). There were no reported significant adverse effects. CONCLUSION: SCN and PECS1 nerve blocks are safe and effective for patients undergoing CIED implantation to minimize or eliminate the use of intravenous sedation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".