Use of Antibacterial Envelopes for Prevention of Infection in Neuromodulation Implantable Pulse Generators
Bibliographic record
Abstract
BACKGROUND: Neuromodulation unit placement carries a historic infection rate as high as 12%. Treatment of such requires surgical removal and a long course of systemic antibiotics. Antibiotic-impregnated envelopes have been effective in preventing infection in implantable cardiac devices. At our center, 1 surgeon uses these envelopes with all implanted neuromodulation units. OBJECTIVE: To assess the efficacy of antibacterial envelopes in prevention of infection in neuromodulation device placement. METHODS: We conducted a retrospective cohort study of consecutive implantable pulse generator (IPG) unit implantation with an antibacterial envelope at a single center between October 2014 and December 2019. We collected demographic data, including postoperative infections, reoperations, and complications, associated with the IPGs. This cohort was then compared with a historical cohort of consecutive patients undergoing surgery before envelope usage (October 2007-April 2014). RESULTS: In the pre-envelope cohort of 151 IPGs placed in 116 patients, there were 18 culture-confirmed infections (11.9%). In the antibacterial envelope cohort of 233 IPGs placed in 185 patients, there were 5 culture-confirmed infections (2.1%). The absolute risk reduction of the antibacterial envelope was 9.85% (95% CI 4.3%-15.4%, P < .01). The number needed to treat was 10.1 (95% CI 6.5-23.1, P < .01) envelopes to prevent 1 IPG infection. CONCLUSION: We saw a reduced rate of infections in the antibacterial envelope cohort. Although this is likely multifactorial, our results suggest a benefit of antibacterial envelopes on infection after neuromodulation surgery.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".